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KatherineSullivanSeniorThesis

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KatherineSullivanSeniorThesis

  1. 1. Commodity Price and Conflict: the Case of Coffee and Uganda* Katherine A. Sullivan Middlebury College Senior Thesis, Spring 2015 Abstract In the past century, over half the countries in the world have experienced civil conflict. A series of recent studies on the economic causes of civil conflict has sought to determine the impact of income on the frequency and intensity of such conflict. Most of the studies have used cross- country datasets and concluded that more within-country case studies are needed to evaluate more precisely the relationship, if any, between income and conflict. The prevailing literature suggests two opposite effects of income on civil conflict: (1) the opportunity cost theory, which posits that an increase in income will result in a decrease in conflict, because the increased income represents a higher wage foregone by those who choose to participate in criminal or illicit activity rather than in the productive sector of the economy; and (2) the rapacity effect theory, which posits that an increase in income will result in an increase in conflict, because the increase in the value of a contestable pool of wealth incites conflict. The application of these theories to specific countries and regions has led to ambiguous results. This case study will test the validity of the competing theories in a particular country. By conducting a natural experiment within historically conflict-prone Uganda—using the variation in global coffee prices as an exogenous measure of income—this study draws on a rich conflict dataset to code every conflict and actor within each district of the country from 2002–2014. This difference-in-difference estimator finds that a rise in global coffee prices results in an increase both in the likelihood of conflict and in the intensity of such conflict differentially in districts cultivating more coffee, which is consistent with the rapacity effect theory. * For his guidance and many helpful comments, I thank my thesis advisor, Professor Erick Gong of the Economics Department at Middlebury College. I am also grateful to my classmates in ECON 701/702, and especially my referee, Stephen Paolillo.
  2. 2. I. Introduction The number of countries experiencing civil conflict at any single point in time increased steadily over the second half of the 20th century, with over 100 countries experiencing a civil conflict since 1945 (World Bank). The loss of life and infrastructure associated with civil conflict inevitably affects a country’s economy—destroying physical and human capital, as well as suppressing and altering political and economic activity. Therefore, understanding the economic influences on civil conflict (both frequency and intensity) is vital for rational policy making. Indeed, the dire consequences of civil conflicts—vast human suffering, destroyed and damaged institutions and infrastructure, and disrupted economic activity—inhibit development in some of the world’s poorest and least developed countries. The destruction of public and private physical capital and reduced capital formation contribute directly to a decline in economic output. Additionally, national and local governments may be forced to reallocate capital and resources from productive activity enhancing education and infrastructure to address the causes and effects of civil conflict (Deininger, 2003). While a spate of economic and political science literature in the late 20th and early 21st century has tried to determine the precise origins of civil conflict, convincing explanations and solutions for specific economic causes are challenging and, as yet, unrealized. Income as the principal economic driver of civil conflict has been studied since the seminal work of Becker (1968) on the economic underpinnings of crime. Becker concluded that an individual makes decisions to minimize the income foregone from licit activity when engaging in illicit activity—a theory that also illuminates the opportunity cost of income when an individual chooses to engage in civil conflict. The opportunity cost theory predicts that as 2
  3. 3. income rises, conflict declines. This theory has been tested against the competing rapacity effect theory, which predicts that a rise in income will lead to a rise in conflict because of the larger pool of contestable wealth. Researchers analyzing these competing theories in studying the causes and consequences of civil conflict routinely use global commodity prices as an exogenous measure of income. While most studies have concluded that commodity prices impact the outbreak of civil conflict and the intensity of such conflict (Anyanwu, 2002; Bazzi and Blattman, 2014; Brückner and Ciccone, 2010; Collier and Hoeffler, 2004), the direction of the impact is unclear, especially at the cross-country level. Cross-country literature has both supported (Brückner and Ciccone, 2010) and found no evidence of the opportunity cost effect (Bazzi and Blattman, 2014). Some cross-country studies support the rapacity effect (Anyanwu, 2002; Collier and Hoeffler, 1998; Hidalgo et al, 2010), while other studies conclude that increased revenue from taxable commodities enables a government to financially quell any potential conflict (Bazzi and Blattman, 2014). Many studies distinguish between labor-intensive and non-labor intensive commodities (Bazzi and Blattman, 2014; Calí, 2015; Dal Bó and Dal Bó, 2011; Dube and Vargas, 2013), and hypothesize that price shocks to labor-intensive, agricultural commodities impact civil conflict through the opportunity cost effect while price shocks to non-labor intensive, extractive commodities affect it through the rapacity effect. This research predicts that stable, easily taxable land resources like extractive commodities are more likely to be seen as a contestable pool of wealth as compared to seasonal, agricultural, labor-intensive commodities, which are more likely to affect only individual income. 3
  4. 4. Within-country case studies have supported the opportunity cost effect, especially when studying labor-intensive commodities (Dube and Vargas, 2013; Calí, 2015), but there have been relatively few within-country case studies. As a result, Bazzi and Blattman (2014) suggest that the most important focus for future research should be on within-country case studies, which seem to be more precise in their results and conclusions. Indeed, the recent case studies of Dube and Vargas (2013) and Calí (2015) have been more consistent in their results and conclusions, and offered more precise explanations for conflict and policy recommendations, than prior studies. Both recent studies confirm that the opportunity cost effect best explains increases in civil conflict when studying labor-intensive commodity prices, but conclude that the rapacity effect best explains the impact of price shocks in extractive commodities. For future research, sub-Saharan Africa is an ideal region in which to conduct a within- country case study because of the dependence by countries in the region on global commodity prices, which provide an exogenous source of income data (global commodity prices), as well as conflict variation over space and time. The economic development of countries in sub-Saharan Africa is routinely threatened because these states are more likely to relapse into civil conflict before a policy can be successfully implemented to address the root causes of the conflict. The region’s economies export primary commodities and very little else, and commodity price downturns have caused slumps in growth and worsening economic conditions (Deaton, 1999). Sub-Saharan African countries such as Burundi, Rwanda, and Uganda depend heavily on coffee exports and were hurt by the loss of income as coffee prices fell over 50% between 1997 and 2000, which was followed by civil conflict in Burundi in 2000, Rwanda in 2001, and Uganda in 2002 (Brückner and Ciccone, 2010; Deininger, 2003). While a multitude of factors may have contributed to make civil conflict likely in those countries, the lack of economic 4
  5. 5. development was a key factor that increased the likelihood and intensity of civil strife (Blattman and Miguel, 2010). This was especially true in Uganda (Deininger, 2003). Uganda’s significant income from coffee exports and the large percentage of its population involved in the chain of production qualifies the country for a natural experiment on the impact of income on civil conflict via variations in global coffee prices. Coffee is a perennial tree crop. Thus, it is categorized as both a labor-intensive and an extractive commodity, because it is labor-intensive, but (like extractive commodities) the producing trees are enduring and taxable. Coffee is especially important in Uganda, which has very limited access to extractive commodities such as oil and rare-earth minerals. As a threshold matter, the expected effect of the competing theories (opportunity cost vs. rapacity effect) as applied to coffee in Uganda is initially unclear, because coffee straddles the boundary between labor-intensive and extractive commodities. The results of both Dube and Vargas (2013) and Calí (2015) would indicate that the opportunity cost effect best explains the link between civil conflict and coffee prices in Uganda, because coffee is a labor-intensive commodity. On the other hand, coffee also has attributes of an extractive commodity, which would indicate that the rapacity effect of commodity prices best explains the impact of global coffee price shocks on civil conflict in Uganda. The purpose of this study is to examine the data on global coffee prices and conflict in Uganda and draw conclusions about the link, if any, between the two. II. Conceptual Framework II.a Opportunity Cost Effect The opportunity cost effect refers to a theory that a change in real income changes incentives for engaging in conflict or other illicit activity. Thus, under this theory, a reduction in 5
  6. 6. real income decreases the income forfeited by a worker choosing to leave the productive sector of the economy to participate in civil conflict. The theoretical work on the impact of income on conflict and crime has concluded that rational individuals weigh the relative returns in their decisions either to predate in the criminal sector through conflict or to work for a wage in the productive sector. As a consequence, instances of civil conflict are expected to fall when incomes rise (Becker, 1968; Dál Bo and Dál Bo, 2011). Household-produced commodities, like agricultural goods, affect incomes directly (Bazzi and Blattman, 2014). The opportunity cost theory would predict that a person’s incentive to rebel and engage in conflict rises as income from agricultural goods or other household-produced commodities declines. Cross-country literature has rarely supported the opportunity cost theory, but within-country case studies have routinely upheld it as the best way to explain the effects of changes in global agricultural commodity prices (Calí, 2015; Dube and Vargas, 2013). II.b Rapacity Effect The rapacity effect refers to a theory that there is an increasing incentive to fight over an economic resource as its value increases, and civil conflicts often have been fought over the control of valuable economic resources. The rapacity effect generally applies to commodities that are: not labor-intensive, highly valuable, not perishable, and easily controlled staples on the land (Calí, 2015). These economic resources are traded in volatile international commodity markets that can produce large positive and negative shocks affecting the value of the commodity at issue. The willingness to engage in conflict to control the production and taxation of (or other income from) commodities is supported by within-country case studies analyzing the impact of oil prices (income from an extractive commodity) on conflict (Calí, 2015; Dube and Vargas, 6
  7. 7. 2013). While some cross-country studies support the rapacity effect (Anyanwu, 2002; Collier and Hoeffler, 2998; Hidalgo et al, 2010), studies like Bazzi and Blattman (2014) conclude that increasing revenue from extractive commodities financially empowers the state to combat any potential uprising. II.c Ambiguity of Commodity Price Shocks Commodity price shocks provide a strong source of exogenous, volatile income variation to coffee-intense districts in Uganda, but should have no effect on a control group of non-coffee intense districts in the country. Uganda is not a top global supplier of coffee, thus unexpected changes in global coffee prices, especially increases due to supply shortages in top producing countries such as Brazil or Columbia, could create contradictory pressures on civil conflict in Uganda. Because coffee is a perennial tree crop that is highly valued and taxable by the state, the rapacity effect makes the contestable pool of wealth a more valuable target over which to fight as global prices increase. On the other hand, because Ugandan coffee is overwhelmingly produced in small household plots, a price increase represents a positive shock to wages, raising the opportunity cost of engaging in conflict. III. Institutional Context: Conflict and Coffee in Uganda III.a Conflict in Uganda Uganda has many of the structural factors accounting for low growth and civil conflict in central African countries, including ongoing land disputes, ethnic diversity, topical diseases and the presence of AIDs, and no access to the ocean. The country is divided into four distinct kingdoms (Buganda, Bunyoro-Kitara, Busoga and Toro)1 and has over sixty indigenous ethnic groups, making Uganda one of the world’s most ethnically diverse states (Deininger, 2003). 1 Background on the four kingdoms is set forth in the Appendix. 7
  8. 8. British rule relied on, and exacerbated, national religious and ethnic divisions to maintain control during the country’s colonial era (Deininger, 2003). While Uganda’s transition to independence in the early 1960s was relatively peaceful, the country’s subsequent post-colonial history has been tumultuous and marked by conflict and human rights violations, culminating in a brutal civil war in the early 1980s. When Milton Obote of the Uganda People’s Congress led the country to independence and became the first Prime Minister in 1962, his political platform strongly opposed the British- favored Buganda Kingdom of southern Uganda (Peace Direct, 2014). With the support of the military, the Obote dictatorship violently disposed of political opposition and was followed by the oppressive dictatorship of Idi Amin from 1971-1979. After the fall of Amin, Obote returned to power, which sparked a brutal civil war in the1980s, and left over half a million people dead (Peace Direct, 2014). In 1986, President Yoweri Museveni of the National Resistance Movement succeeded Obote,2 and became the first leader from southern Uganda in over a decade. He enacted a wide range of reforms at all levels of government and society, and is credited with substantially improving human rights in the country, specifically by curbing abuses by the army (Peace Direct, 2014). He devalued the Ugandan schilling, privatized companies, reduced the size of the armed forces, created a new Constitution in 1995 that restored the four kingdoms, and dissolved state monopolies over cotton and coffee. These reforms produced solid economic growth (Deininger, 2003). Despite Museveni’s success in restoring some stability to the country, Uganda has suffered from rampant corruption and averages a 2.7/10 in Transparency International’s annual 2 Museveni and the National Resistance Movement rebelled against Obote after the 1980 elections, which were widely believed to have been rigged. Museveni declared himself President of Uganda in 1986, and was elected to the post in 1996. He has continued to be reelected and is still President of Uganda (Britannica Encyclopedia). 8
  9. 9. corruption perceptions index over the last three years (Transparency International, 2014). This level of actual and perceived corruption has led to protests after Museveni’s abolishment of presidential term limits, and to riots after he was rumored to have bribed voters in the 2011 election (Peace Direct, 2014). The threat of conflict in reaction to Museveni’s undemocratic policies, along with the violence of insurgent groups from the northern and western regions, has led many in the international community to fear greater instability in Uganda and the region. After another northern rebel group failed to overthrow the Museveni government in 1987, Joseph Kony founded the Lord’s Resistance Army (LRA) to rebel against the president (and his supporters from southern Uganda). The LRA, which is primarily composed of ethnic Acholis, is responsible for nearly twenty years of conflict, the abduction of over 20,000 children, the mutilation and murder of many victims, the displacement of 2 million people (nearly the entire affected population in the north), and increased tensions between northern and southern Uganda (Arieff and Ploch, 2014). In 2006, the Ugandan military succeeded in pushing the LRA out of the country and shifted that theater of conflict from northern Uganda to the Democratic Republic of Congo, the Central African Republic, and the Republic of South Sudan. Conflicts involving rebel groups in Uganda are not limited to the northern districts. In the years since Museveni came to power, more than twenty other militant groups have attempted to displace the government, including the Allied Democratic Front operating in the western districts of Uganda and groups operating on the western border with the Democratic Republic of Congo (Peace Direct, 2014). In the eastern districts, armed rebellion persists as the government conducts a forceful disarmament program (Peace Direct, 2014). Thus, there is wide regional variation in conflict in the country. 9
  10. 10. The potential for escalating conflict is largely driven by competition for influence and power over land (Rugadya, 2009). Along with Museveni’s large-scale economic and political reforms, Uganda also has experienced a large increase in the number of governing districts— rising from 34 in 1991 to 111 (plus the capital city) today. District creation has improved service delivery and created jobs, but has not rid the country of ethno-linguistic conflict, because the redrawing and creation of districts has resulted in disputes between ethnic groups over district dominance (Green, 2008). Conflicts over land rights have long plagued Uganda, stemming from the 1900 Buganda agreement3 and the nationalization of land under Amin’s 1975 land decree. As a result, the country has a significant number of cases of overlapping property rights. Government interventions to curb land conflict have been unsuccessful so far, and tensions over land rights and other resources have the potential to grow into larger ethnic or religious conflicts due to existing “notion[s] of ethno-territorialism and the contested politics of belonging,” (Rugadya, 2009). Because Uganda is an agrarian economy, the value of land is very high “as a socioeconomic asset, where wealth and survival are measured by control of, and access to land” (Rugadya, 2009). Therefore, if land values increase in a political environment where certain ethnic groups are perceived as indigenous and other minority ethnic groups are excluded or disenfranchised as districts are drawn and redrawn, this could be a significant precursor to civil conflict. III.b Ugandan Coffee History Coffee is the second highest valued commodity in international trade after petroleum and the most widely traded tropical agricultural commodity (International Coffee Organization, 3 See Appendix for details. 10
  11. 11. 2003). It is one of the key traded commodities for least developed countries and a vital source of foreign exchange, cash income, and employment for these underdeveloped economies. African countries collectively account for 14% of the world’s coffee exports, and Uganda is the second largest exporter in Africa (Kraybill and Koidido, 2009). Coffee is the leading export sector in Uganda’s agrarian economy, providing income to over 3.5 million people who work at all of the different levels of the domestic value chain—from living and working on farms to involvement in other downstream businesses in the production, marketing and exportation processes (DIMAT, 2012). The Uganda Coffee Trade Federation estimates that 20% of the country’s entire population earns all or a large part of their income from coffee. These farmers and workers thus depend on the export price of coffee for their income. The export price of coffee has been buffeted by the volatility of the international coffee market since Museveni liberalized the domestic market for coffee in Uganda in the 1992. Prior to the abolishment of the state-controlled Coffee Marketing Board, farmers received controlled low prices for their beans. The establishment of the Uganda Coffee Development Authority, however, allowed market forces of supply and demand to determine prices, and producers’ share of the export prices for coffee has increased significantly (Ahmed, 2012). As shown in Figure 3, the price that producers receive directly tracks the export price of coffee sold on the global market. Thus, the price that coffee growers in Uganda receive for their production is guided by traders in the commodity market, showing how liberalization of the coffee market by Museveni led to commodity markets in New York and London determining prices and income in Uganda (Ahmed, 2012). 11
  12. 12. Coffee has remained Uganda’s top export earner—representing, on average, 18% of Uganda’s total export earnings between 2000-2010 (Ahmed, 2012), and approximately 3.78% of Uganda’s GDP.4 As shown in Figure 4, domestic consumption of coffee in Uganda is relatively low, ranging from only 4% to 10% of the total production, thereby making coffee primarily an export crop (Ahmed, 2012). The large percentage of coffee production that is used for export means that any shocks to the Ugandan coffee sector will be exogenous, because only a small percentage of income comes from the domestic market. This exogenous variation is a critical assumption in utilizing global coffee prices as a proxy for income variation. IV. Data and Methodology IV.a Data The conflict dataset is publicly accessible online from the Armed Conflict Location & Event Data Project (ACLED). ACLED codes all reported political violence events by exact date and location for over 50 developing countries. The dataset covers Uganda from 1997 to the present and includes 9 specific conflict event types—battle with no change of territory, battle where a non-state actor overtakes territory, battle where the government regains territory, non- violent transfer of territory, establishment of a headquarters or base, non-violent activity by a conflict actor, riots/protests, violence against civilians, and remote violence. The dataset codes each conflict by date, district location, actors and allies involved, number of fatalities, exact geographic location, and source of the event report. I have aggregated the conflicts at the district-month level using 2002 district boundaries, and coded a series of dummies for whether or not any type of conflict occurred, for whether or not a specific type of conflict occurred, and for whether or not the LRA was involved in a 4 This figure was calculated from the CIA’s Country Profile showing that exports make up 21% of Uganda’s GDP and the percentage of Uganda’s total export earnings derived from coffee exports (18%). 12
  13. 13. conflict. To measure the intensity of conflict, the variables fatalities (measuring the number of fatalities) and conflictnum (the number of conflicts) are generated on the district-month level. Some conflict event types, such as “Violence Against Civilians” and “Battle: No Change of Territory,” occur substantially more frequently than others, such as “Remote Violence” and “Establishment of Headquarters or Base.”5 All violent forms of conflict, including “Battle: Government Regains Territory,” “Battle: No Change of Territory,” “Battle: Non-state Actor Overtakes Territory,” “Remote Violence” and “Violence Against Civilians,” occur more frequently than non-violent conflict, and are aggregated in the dummy variable violentconflict while non-violent/remote forms of conflict are coded as 0. I generated a dummy variable changeterritory that indicates whether “Battle: Non-state Actor Overtakes Territory” or “Battle: Government Regains Territory” occurred. Since changes in global coffee prices can affect the value of underlying land, I have examined if conflicts involving a change in territory are more likely. Because of the limited availability of data on coffee production, I use conflict data only for the years when coffee production data is available at the beginning of the sample period: 2002 through 2014. To measure whether coffee prices increased the incidence or the intensity of conflict differentially in districts cultivating more coffee, I acquired cross-sectional, district-level data on the percentage of agricultural households that have a coffee plot from a 2002 Uganda Bureau of Statistics (UBOS) Population and Housing Census (PHC) report on agriculture.6 5 The attached Table 1 shows the summary statistics for the conflict, agricultural census, relevant district-level characteristics, and the global coffee price. The variables generated to measure the different types of conflict are defined in the table, and the mean measures the frequency of that form of conflict. As discussed above, the means for the “Battle: No Change of Territory” and “Violence Against Civilians” variables are the highest, while the number of conflicts per district per month that involved the LRA is also high. 6 The PHC report is defined as “a summary of the main findings from the data collected by the Uganda Bureau of Statistics (UBOS) through the Agricultural Module (AM) attached to the Population and Housing Census (PHC) 2002 that was conducted in September 2002.” 13
  14. 14. Additional cross-sectional district-level data from 2002 is available for 15 other district- level characteristics from Annex 1 of the 2002 PHC report: population, urbanization level, median age, share of population under 18 years of age, age dependency ratio, literacy rate, primary net enrollment rate (as an alternative measure of education), percentage of the main source of livelihood arising from subsistence farming, earned income and other sources, percentage of the working population in the manufacturing sector, service sector and in subsistence farming, the unemployment rate, and the percentage of houses with basic necessities (as a measure of poverty). These district-level characteristics were used in robustness checks to test that there is no correlation between district-level characteristics and coffee cultivation that might be affected by global coffee prices. Since these characteristics were available only for 2002, they are interacted with global coffee prices. In the 2002 PHC report’s table of district-level characteristics, the data is aggregated at the regional level, and provides a list of districts by region. Regional governments preside over the district governments and all fall under the authority of President Museveni’s national government. By generating regional identification variables (region1 through region4) for Uganda’s four major regions (Eastern, Northern, Central and Western), I can generate regional time trends by interacting these variables with a year identifier. These regional time trends account for potential omitted variables because coffee is cultivated more intensely in particular districts, and violence may be trending a certain way in those areas based on other factors such as varying economic growth or the movement and presence of the LRA or other armed groups. Global coffee price data is available from the International Monetary Fund (IMF), which tracks global commodity prices on a monthly basis.7 Price data for Arabica and Robusta coffees 7 The publicly accessible online database provides monthly prices in U.S. dollars for all internationally traded commodities from 1980 to the present. 14
  15. 15. are measured in U.S. cents per pound and obtained on a monthly basis from 2002-2014. Uganda primarily cultivates and exports the Robusta brand of coffee—only few specific districts on the east and west sides of the country are even suited for Arabica production—therefore only global Robusta prices are used in the model.8 IV.b Methodology To analyze how exogenous income shocks impact civil conflict, I build on the research of Dube and Vargas (2013) and use a single conflict-prone state as a case study. Specifically, I am investigating how changes in income caused by global commodity prices affect the likelihood and intensity of civil conflict in Uganda. A naïve regression of civil conflict on income would suffer from omitted variable bias and reverse causality. There would be a substantial number of omitted variables correlated with both income and civil conflict. Additionally, reverse causality would plague this naïve regression, because if a conflict occurs in a specific region, it will have a substantial economic impact that will also affect income. To avoid these econometric obstacles, I conduct a case study in Uganda using global commodity prices to generate plausibly exogenous variation in income in districts where coffee is the dominant crop. Using monthly, district-level conflict data and monthly international coffee prices during the 2002 to 2014 time period, I assess whether international coffee price changes impact the frequency or intensity of conflict disproportionately in districts that produce more coffee. It is critical that I use data on district-level coffee production from 2002, because if production were measured after the beginning of the sample period, then it could reflect remnants of conflict from the beginning of the sample period as well as past periods of high or 8 As shown in Figure 1 in the Appendix, Robusta and Arabica prices are highly correlated. Since the correlation coefficient is greater than 0.8, and only select districts cultivate Arabica, global Robusta prices will be used as the measure for the variation in coffee prices over time. 15
  16. 16. low coffee prices. High (low) prices would incentivize districts to substitute into (out of) coffee production, thus introducing measurement error and biasing the estimates. Furthermore, if Uganda exported enough coffee to impact the world price, then I would be concerned about reverse causality—civil conflict could limit the supply of Ugandan coffee and raise the world price. Since Uganda both produces and exports significantly less coffee than the top 5 exporters, I am not concerned with reverse causality in the model.9 The main regression estimating the effect of commodity shocks on conflict is given by: Ydjt = α + λd + γjt + β1 (Cofd × CPjt) + Xrt + εdjt where Ydjt are the conflict outcomes10 in district d, month j, and year t; α is the estimated intercept; λd are district fixed effects; γjt are fixed effects for month j in year t; Cofd is the percentage of agricultural households that have a coffee plot in district d; CPjt is the natural log of global coffee prices in month j and year t; and Xrt are regional time trends for Uganda’s four major regions (Eastern, Northern, Central and Western). Dube and Vargas (2013) examine a similar question, studying the impact of commodity prices on civil conflict in Colombia. Using international price movements of both coffee and oil, they test whether an exogenous price shock impacts conflict differentially in areas of the country producing disproportionately more coffee or oil. Dube and Vargas measure coffee production intensity for each municipality in 1997—the middle of their sample period. To correct for measurement error, they use rainfall and temperature as instrumental variables (IVs) for coffee productivity in each municipality. Because Colombia is consistently one of the top global 9 During 2002 to 2014, Uganda accounted for between 2% and 5% of the world’s coffee exports, and has never been a top five global exporter of coffee (USDA, 2014). 10 Conflict outcomes include: the number of fatalities and the number of conflicts, as well as the conflict, violentconflict and changeterritory dummies. 16
  17. 17. exporters of coffee, they also correct for reverse causality by using an IV of the interaction of world price with the export volume of the other leading coffee exporting nations: Brazil, Venezuela, and Indonesia. With this instrument, movements in international price are exogenous, because they are driven by the export supply of other countries. Dube and Vargas studied Colombia, a country that is not as dependent on primary commodity exports as Uganda and that has a higher level of export diversification than Uganda. But because Uganda is dependent on a primary commodity export, which can test both the rapacity effect and the opportunity cost mechanisms, I will be studying only the impact of global coffee prices on civil conflict. Since international price shocks are exogenous and there is data on production intensity at the beginning of my sample period, I will not be using Dube and Vargas’s IVs. V. Results V.a Extensive Margin Results Table 2 presents the effects of coffee prices on the extensive margin: I measure income’s effect on the likelihood of conflict. The statistically significant, positive coefficient in OLS (Column 1) remains unchanged with the addition of district and time-fixed effects in Columns 2 and 3. The positive coefficient measures the differential impact of global coffee prices on conflict between coffee-intense districts and non-coffee intense districts and supports the rapacity effect theory that the likelihood of conflict increases as income increases. The point estimate is 0.011, estimating that for the 435.14% increase in global coffee prices between the beginning of the sample period in January 2002 and the peak in early 2008, the likelihood of conflict increased by 0.048 percentage points. 17
  18. 18. My results may be overestimating the relative increase in conflict in coffee-intense districts compared to non-coffee intense districts, because coffee prices may not be the only variable changing over time that impacts conflict. Therefore, I include regional time trends in Column 4 as a robustness check to measure if anything else is changing over time that could explain a change in conflict in the districts. For example, the LRA or other active rebel groups could have increased their presence and engaged in conflict in certain coffee-intense districts, motivated by political or religious incentives rather than changes in income. Even though the positive coefficient is still statistically significant at the 10% level in Column 4, the inclusion of regional time trends decreases both the magnitude and precision of the estimate. While my results are still consistent with the rapacity effect, there are major regional trends that need to be accounted for, and lead to an upward bias if omitted. With the inclusion of regional time trends, the model estimates that for the 435.14% increase in global coffee prices, the likelihood of conflict will increase by 0.017 percentage points. Columns 5 and 6 of Table 2 show regressions of the interaction of a positive price shock with the intensity of coffee production and a negative price shock with the intensity of coffee production respectively. A positive price shock occurs when Robusta prices rise 2 standard deviations above the mean of 78.36 cents per pound; while a negative price shock arises when Robusta prices fall 2 standard deviations below the mean. Both coefficients are statistically significant at the 1% level (without using regional time trends), and estimate that in the presence of a positive price shock in Column 5, the likelihood of conflict will increase differentially in districts cultivating more coffee, while the negative coefficient in Column 6 shows that a negative price shock decreases the likelihood of conflict. Both results are consistent with the rapacity effect. 18
  19. 19. My second set of results specifically examines the rapacity effect by measuring the impact of coffee prices on conflicts that involve a change of territory (Table 3). The coefficient is both positive and statistically significant at the 5% level in OLS (Column 1) as well as in Columns 2 and 3 with the inclusion of district and time fixed effects; however, the precision of the estimate is reduced by the addition of regional time trends in Column 4. These results are consistent with Table 1 in their support for the rapacity effect, but the magnitude of the point estimate is small. The change of the territory dummy variable does not measure the number of conflicts fought over a territory, but rather indicates only conflicts where territory changed hands between parties. The small magnitude shows how infrequently territory is actually won or lost. Columns 5 and 6 measure the effect of a positive or negative price shock, and my results are quantitatively similar to previous results supporting the rapacity effect. V.b Intensive Margin Results Table 4 presents the effects of coffee prices on the intensive margin: I measure income’s effect on the intensity of conflict measured by number of fatalities. The statistically significant, positive coefficient in OLS (Column 1) remains unchanged in Columns 2 and 3 with the addition of district and time fixed effects; the point estimate is 0.275, estimating that for the 435.14% increase in global coffee prices between 2002 and 2008, the intensity of conflict increased by 1.197 fatalities. This positive, statistically significant coefficient supports the rapacity effect theory that the intensity of conflict increases as income increases. The estimate loses precision and magnitude, however, with the addition of regional time trends in Column 4. Using a different measure of price changes, the presence of a positive price shock has a positive, statistically significant effect on the intensity of conflict in Column 5, and the presence of a negative price 19
  20. 20. shock has a negative, statistically significant coefficient in Column 6, further supporting the rapacity effect. My second set of results on the intensive margin measures income’s effect on the number of conflicts (Table 5). Like Table 4, the coefficient is both positive and statistically significant at the 5% level in OLS (Column 1) as well as in Columns 2 and 3 with the inclusion of district and time fixed effects; the precision of the estimate, however, is reduced by the addition of regional time trends in Column 4. Columns 5 and 6 measure the effect of a positive or negative price shock, and those results are also quantitatively similar to previous results supporting the rapacity effect. In sum, the results on both the extensive and intensive margin show that as global coffee prices increase, the likelihood and intensity of conflict increases on average, holding all else constant. The positive coefficients on the interaction between the log of Robusta prices and intensity of coffee production might alternatively be interpreted as indicating that when prices rose, violence fell, but to a lesser degree in coffee-intense districts. This alternative interpretation still shows that an increase in global coffee prices relatively increases conflict in districts that produce more coffee than the non-coffee intense districts. More concerning, however, is how these positive coefficients may be overstating the relative increase in conflict in coffee-intense districts, because income from coffee exports represents a percentage of national income that could be causing spillover effects into the natural experiment’s control group of non-coffee intense districts. A positive economic stimulus from coffee’s impact on national income could have a conflict-reducing impact in non-coffee regions, because an improvement in economic conditions has been associated with an overall decrease in 20
  21. 21. conflict (Deininger, 2003; Blattman and Miguel, 2010). If coffee price increases actually decrease conflict in non-coffee intense regions via national income changes, this results in an overestimation of the conflict increase in coffee-intense regions as a result of the price increase. However, assuming that national income effects are the same in coffee-intense districts when compared to non-coffee intense districts, the estimates are unbiased. This is a standard assumption used in the prior literature (Dube and Vargas, 2013). V.c Comparison to Dube and Vargas (2013) Dube and Vargas (2013) conclude that the opportunity cost effect best describes the relationship between income and conflict when using global coffee prices as the exogenous variation in income. For oil, however, the authors find that using global oil prices as the exogenous variation in income interacted with oil production across Columbia’s municipalities supports the rapacity effect theory. Unlike Dube and Vargas, my current study supports the rapacity effect using the same global coffee prices used by Dube and Vargas as a measure of income. The difference in the two studies could be explained by Uganda’s history of land conflicts. An econometric study by Deininger (2003) has shown that within Uganda, more so than other countries, greater coffee production increases conflict and that coffee could be considered one of the more stable and economically valuable land resources in Uganda, similar to oil in Colombia. Taking this finding into account makes my study and its conclusions consistent with the conclusions of Dube and Vargas. The persistent tensions between ethnic groups in Uganda, as the country continues to add and divide governmental districts, will likely provide continued incentives for groups to fight over disputed land as its value increases with global coffee prices. Moreover, the country’s 21
  22. 22. politically sensitive problem of overlapping property rights—disputed since the 1900 Uganda Agreement—also could encourage conflict over land, should rising coffee prices make the disputed land more valuable. In the past decade, significant literature and studies have emerged regarding Uganda’s history of civil conflict and the country’s heavy reliance on an agricultural export like coffee. Deininger (2003) concludes that an increase in taxable income increases the propensity towards conflict, which is consistent with the rapacity effect. The increase in taxable income arises from the perennial, high-value coffee trees that require an initial capital investment and are an easy resource to tax. As these trees become more valuable, taxable income will increase and, as Deininger (2003) concludes, so will civil conflict. In Colombia, coffee does not qualify as a national prize to be won through rebellion as much as oil does. As an extractive, capital-intensive commodity, oil and its price increases are associated with greater state revenue rather than larger family incomes, which are usually more reliant on commodities grown through subsistence farming. Therefore, the association between oil and the state incentivizes rebellion over oil resources, because the state has a greater claim on increasing wealth than a worker does over their income from these extractive resources. Uganda, on the other hand, does not have an abundance of extractive commodities from which income accrues directly and substantially to the state. Therefore, as a perennial crop, coffee could be one of the most stable land resources in the Ugandan agrarian economy, because the trees can be taxed more easily than maize, fish, or any of the other top contributors to Uganda’s GDP. The perennial tree crop acts as an extractive, capital-intensive resource in the agrarian economy, and therefore, could fit Dube and Vargas’s explanation for their significant positive coefficient on their interaction between the log of global oil prices and oil production. 22
  23. 23. VI. Conclusion This study has analyzed the relationship between income and civil conflict by conducting a natural experiment using global commodity prices as an exogenous measure of variation in income. Utilizing the ACLED dataset coding for conflict outbreak and intensity, I design a country case study in commodity export-dependent Uganda—a country that has a rich history of political and land conflict. Through my difference-in-difference estimation, I demonstrate that a rise in the global price of coffee increases the likelihood and intensity of conflict violence differentially in districts that cultivate coffee more intensively, and from these results conclude that the rapacity effect best explains the relationship between income and civil conflict. Because coffee has historically accounted for a significant percentage of Uganda’s GDP and land conflicts have burdened the country since British colonial rule, the rapacity effect best explains the relationship between income and conflict in Uganda. As a perennial tree crop, coffee trees are typically high-value because they require an initial capital investment and can be targets for taxation (Bazzi and Blattman, 2014). The rapacity effect explains how global coffee prices could impact civil conflict in Uganda: as the value of land bearing coffee trees increases with global coffee prices determined by the free market, this can ignite conflict simmering from pre- existing tensions between ethnic groups fighting for dominance within a newly formed district or between neighbors/competitors with overlapping land rights. This study also highlights several policy implications. Because the Ugandan government has yet to effectively resolve the problem of overlapping land title disputes, the rapacity effect theory shows that tensions over land disputes can escalate into conflict as the value of land assets increases from the rise in global coffee prices. The severe economic, political, and social consequences that arise from civil conflict, which plagued Uganda in the 1980s, should 23
  24. 24. incentivize the government to invest in definitively resolving the sensitive land title disputes issue. The rapacity effect demonstrates the role of greed as a trigger of conflict, and improved access to stable income from either farm or non-farm sources may reduce incentives to engage in conflict over the high value of a contestable resource. This research contributes to the growing pool of literature analyzing the economic causes of conflict by supplementing the within-country case studies quantifying the relationship between income and civil conflict. Since my results support the rapacity effect and thereby challenge Dube and Vargas’s (2013) conclusion on the opportunity cost effect, it should spur more within-country case studies to analyze the effect of global commodity prices on civil conflict, especially within countries whose ethnic, political, and economic structures differ substantially from both Colombia and Uganda. 24
  25. 25. References Ahmed, M., 2012. “Analysis of Incentives and Disincentives for Coffee in Uganda.” Technical Notes Series, MAFAP, FAO. Anyanwu, John C. 2002. “Economic and Political Causes of Civil Wars in Africa: Some Econometric Results.” African Development Bank Economic Research Papers. No. 73. Arieff, Alexis and Lauren Ploch. 2014. “The Lord’s Resistance Army: The U.S. Response.” Congressional Research Service: R42054, 15 May. Bazzi, Samuel and Christopher Blattman. 2014. “Economic Shocks and Conflict: Evidence from Commodity Prices.” American Economic Journal: Macroeconomics. Becker, Gary. 1968. “Crime and Punishment: An Economic Approach.” The Journal of Political Economy. 76: 169-217. Blattman, Christopher and Edward Miguel. 2010. “Civil War.” Journal of Economic Literature. 48(1): 3-57. Brückner, Markus and Antonio Ciccone. 2010. “International Commodity Prices, Growth and Outbreak of Civil War in Sub-Saharan Africa.” The Economic Journal. 120(544): 519- 534. Bussolo, Maurizio, Olivier Godart, Jann Lay and Rainer Thiele. 2006. “The Impact of Coffee Price Changes on Rural Households in Uganda.” Policy Research Working Papers. World Bank: WPS4088. Calì, Massimiliano. 2015. “Trading Away from Conflict: Using Trade to Increase Resilience in Fragile States.” Directions in Development. World Bank. Collier, Paul, and Anke Hoeffler. 1998. “On Economic Causes of Civil War.” Oxford Economic Papers. 50: 563-573. Collier, Paul, and Anke Hoeffler. 2004. “Greed and Grievance in Civil War.” Oxford Economic Papers. 56: 563-595. Dal Bó, Ernesto, and Pedro Dal Bó. 2011. “Workers, Warriors and Criminals: Social Conflict in General Equilibrium.” Journal of European Economic Association. 9(4): 646-677. Deaton, Angus. 1999. “Commodity Prices and Growth in Africa.” Journal of Economic Perspectives. 13(3): 23-40. Deininger, Klaus. 2003. “Causes and Consequences of Civil Strife: Micro-level Evidence from Uganda.” Oxford Economic Papers. 55(4): 579-606. 25
  26. 26. Deininger, Klaus and John Okidi. 2003. “Growth and Poverty: Reduction in Uganda, 1999-2000: Panel Data Evidence.” Development Policy Review. 21(4): 481-509. Development of Inclusive Markets in Agriculture and Trade (DIMAT). 2012. “The Market and Nature of Coffee Value Chains in Uganda.” United Nations Development Program. Dube, Oeindrila and Juan Vargas. 2013. “Commodity Price Shocks and Civil Conflict: Evidence from Colombia.” Review of Economic Studies. 80(4): 1384-421. Encyclopedia Britannica. 2015. “Yoweri Kaguta Museveni: President of Uganda.” January, (http://www.britannica.com/bps/user-profile/4419/the-editors-of-encyclopaedia- britannica). Green, Elliott. 2008. “District Creation and Decentralization in Uganda.” Development as State Making. Working Paper No. 24. Hidalgo, Daniel F., Suresh Naidu, Simeon Nichter and Neal Richardson. 2010. “Occupational Choices: Economic Determinants of Land Invasions.” Review of Economics and Statistics. 92(3): 505-523. International Coffee Organization. 2003. “Impact of the Coffee Crisis on Poverty in Producing Countries.” International Coffee Council Background Paper ICC-89-5 Rev 1. International Monetary Fund (IMF). 2014. “Monthly Data.” IMF Primary Commodity Prices. May (http://www.imf.org/external/np/res/commod/index.aspx) Kraybill, David and Michael Kidoido. 2009. “Analysis of Relative Profitability of key Ugandan Agricultural Enterprises by Agricultural Production Zone.” International Food Policy Research Institute. USSP Background Paper no. USSP 04. Kurian, George Thomas. 1992. “Uganda—Ethnic Groups.” Encyclopedia of the Third World, 4th Edition, Volume III. Facts on File: New York, NY. Maystadt, Jean-Francois and Olivier Ecker. 2014. “Extreme Weather and Civil War: Does Drought Fuel Conflict in Somalia through Livestock Price Shocks?” American Journal of Agricultural Economics. Miguel, Edward, Shanker Satyanath, and Ernest Sergenti. 2004. “Economic Shocks and Civil Conflict: An Instrumental Variable Approach.” Journal of Political Economy. 112(4): 725-753. Peace Direct. 2014. “Uganda: Conflict Profile.” Insight on Conflict, February (http://www.insightonconflict.org/conflicts/uganda/conflict-profile/). Raleigh, Clionadh, Andrew Linke, Håvard Hegre and Joakim Karlsen. 2010. “Introducing 26
  27. 27. ACLED-Armed Conflict Location and Event Data.” Journal of Peace Research. 47(5) 1- 10. The Stockholm International Peace Research Institute. 2015. SIPRI Yearbook 2014. Oxford University Press, Oxford. Tibaidhukira, Sarah Kayanga. 2014. “Agricultural Sector Budgetary Allocations in Uganda.” The Eastern Africa Farmers’ Federation (EAFF). Transparency International. 2015. “Corruption Perceptions Index 2014: Results.” Corruption Perceptions Index 2015 Brochure, December, (http://www.transparency.org/cpi2014/results/). Uganda Bureau of Statistics (UBOS). 2004. “Report on the Agricultural Module, Piggy-Backed onto the Population and Housing Census (PHC), 2002: Household Based Agricultural Activities, Crop, Livestock and Poultry Characteristics.” Republic of Uganda, UBOS. Uganda Coffee Trade Federation. 2010. “Response and Comments to the Draft proposed National Coffee Policy.” Ministry of Agriculture, Animal Industry and Fisheries: March, 2010 United States Department of Agriculture (USDA). 2014. “Coffee: World Markets and Trade.” Foreign Agricultural Service Note: December 2014. World Bank, 2010. “World Development Report 2011: Conflict Security and Development.” The World Bank, Washington. 27
  28. 28. Figures and Tables Figure 1: District-level coffee production in Uganda Source: Uganda Bureau of Statistics (UBOS). 2004. “Report on the Agricultural Module, Piggy-Backed onto the Population and Housing Census (PHC), 2002: Household Based Agricultural Activities, Crop, Livestock and Poultry Characteristics.” Republic of Uganda, UBOS. Figure 2: Variation in conflict intensity in Uganda Source: Raleigh, Clionadh, Andrew Linke, Håvard Hegre and Joakim Karlsen. 2010. “Introducing ACLED-Armed Conflict Location and Event Data.” Journal of Peace Research. 47(5) 1-10. 28
  29. 29. Figure 3: Export prices and price producer receives Source: Ahmed, M., 2012. “Analysis of Incentives and Disincentives for Coffee in Uganda.” Technical Notes Series, MAFAP, FAO. Figure 4: Coffee production, domestic coffee consumption and amount of coffee exported Source: Ahmed, M., 2012. “Analysis of Incentives and Disincentives for Coffee in Uganda.” Technical Notes Series, MAFAP, FAO. 29
  30. 30. Table 1: Summary Statistics Variable Observations Mean Standard Deviation Number of Fatalities 8736 1.08 9.50 Number of LRA Conflicts 8736 0.20 1.56 Battle: Government Regains Territory 8736 0.001 0.03 Battle: No Change of Territory 8736 0.14 0.92 Battle: Non-State Actor Overtakes Territory 8736 0.001 0.03 Headquarters or Base Established 8736 0.003 0.12 Non-Violent Activity by a Conflict Actor 8736 0.03 0.24 Remote Violence 8736 0.004 0.08 Riots/Protests 8736 0.06 0.44 Violence Against Civilians 8736 0.14 0.89 Global Robusta Prices 8736 78.36 29.56 Agricultural Households 8736 68455.09 36041.54 Agricultural Households with Coffee Plots: Total 8736 4314.46 6074.59 Agricultural Households with Coffee Plots: Percent Plots 8736 5.74 6.88 Total Number of Coffee Plots 8736 6087.82 8741.07 30
  31. 31. Table 2: Outbreak Results Dependent variable: conflict OLS Fixed Effects Fixed Effects Fixed Effects Fixed Effects Fixed Effects (1) (2) (3) (4) (5) (6) Percent Coffee Plots -0.054*** (0.016) Log Robusta -0.123*** -0.123*** (0.045) (0.045) Log Robusta x Percent Coffee Plots 0.011*** 0.011*** 0.011*** 0.004* (0.004) (0.004) (0.004) (0.002) Positive Shock x Percent Coffee Plots 0.008*** (0.002) Negative Shock x Percent Coffee Plots -0.010*** (0.004) District Fixed Effects N Y Y Y Y Y CMC Fixed Effects N N Y Y Y Y Regional Time Trends N N N Y N N Number of Observations 8,736 8,736 8,736 8,736 8,736 8,736 Number of Districts 56.000 56.000 56.000 56.000 56.000 note: *** p<0.01, ** p<0.05, * p<0.1
  32. 32. Table 3: Change of Territory Results Dependent variable: changeterritory OLS Fixed Effects Fixed Effects Fixed Effects Fixed Effects Fixed Effects (1) (2) (3) (4) (5) (6) Percent Coffee Plots -0.002** (0.001) Log Robusta -0.008** -0.008** (0.003) (0.003) Log Robusta x Percent Coffee Plots 0.000** 0.000** 0.000* 0.024 (0.000) (0.000) (0.000) (0.015) Positive Shock x Percent Coffee Plots 0.000** (0.000) Negative Shock x Percent Coffee Plots -0.001* (0.000) District Fixed Effects N Y Y Y Y Y CMC Fixed Effects N N Y Y Y Y Regional Time Trends N N N Y N N Number of Observations 8,736 8,736 8,736 8,736 8,736 8,736 Number of Districts 56.000 56.000 56.000 56.000 56.000 note: *** p<0.01, ** p<0.05, * p<0.1
  33. 33. Table 4: Conflict Intensity Results Dependent variable: fatalities OLS Fixed Effects Fixed Effects Fixed Effects Fixed Effects Fixed Effects (1) (2) (3) (4) (5) (6) Percent Coffee Plots -1.282** (0.506) Log Robusta -4.003*** -4.003*** (1.459) (1.459) Log Robusta x Percent Coffee Plots 0.275** 0.275** 0.275** 0.111** (0.110) (0.110) (0.111) (0.052) Positive Shock x Percent Coffee Plots 0.123** (0.048) Negative Shock x Percent Coffee Plots -0.290** (0.122) District Fixed Effects N Y Y Y Y Y CMC Fixed Effects N N Y Y Y Y Regional Time Trends N N N Y N N Number of Observations 8,736 8,736 8,736 8,736 8,736 8,736 Number of Districts 56.000 56.000 56.000 56.000 56.000 note: *** p<0.01, ** p<0.05, * p<0.1
  34. 34. Table 5: Conflict Occurrence Results Dependent variable: conflictnum OLS Fixed Effects Fixed Effects Fixed Effects Fixed Effects Fixed Effects (1) (2) (3) (4) (5) (6) Percent Coffee Plots -0.321** (0.137) Log Robusta -0.896** -0.896** (0.402) (0.402) Log Robusta x Percent Coffee Plots 0.068** 0.068** 0.068** 0.024 (0.030) (0.030) (0.030) (0.015) Positive Shock x Percent Coffee Plots 0.123** (0.013) Negative Shock x Percent Coffee Plots -0.290** (0.032) District Fixed Effects N Y Y Y Y Y CMC Fixed Effects N N Y Y Y Y Regional Time Trends N N N Y N N Number of Observations 8,736 8,736 8,736 8,736 8,736 8,736 Number of Districts 56.000 56.000 56.000 56.000 56.000 note: *** p<0.01, ** p<0.05, * p<0.1
  35. 35. Appendix I. Overviews of the Kingdoms of Uganda Uganda is currently divided into four kingdoms—Buganda, Bunyoro-Kitara, Busoga and Toro. Buganda is the largest of the kingdoms and is the namesake of the country, accounting for 17% of its population (Kurian, 1992). It is located in the south-central region of the country and is home to the nation’s political and commercial capital, Kampala. Bunyoro-Kitara is located in mid-western Uganda and is the second largest of the Ugandan kingdoms (Kurian, 1992). It was once an empire that controlled the most of the land that is currently Uganda, and 77% of its people live on subsistence agriculture. Busoga is smaller than Bunyoro-Kitara and Buganda. Toro was once part of the Bunyoro Empire, but was founded by the son of its King in 1830 (Kurian, 1992). The number of districts currently in each kingdom has been constantly changing as districts are divided and added. Uganda can also be classified into several broad linguistic groups—the Bantu-speaking majority in the central, southern and western regions, and the non- Bantu speakers in the eastern and northern parts of the country (Kurian, 1992). The 1900 Uganda Agreement redistributed 19,600 square miles of land under British colonial rule. The British awarded an estimated 958 square miles to the Buganda King and 8,000 square miles were divided among 1,000 ethnic leaders and private landowners, (Deininger, 2003). The remainder of the land was declared Crown Land and stayed under government control. This agreement implied that peasants had no ownership rights over their land, but were considered tenants with little security against eviction (Deininger, 2003). Changes in ownership rights and the proliferation of peasant tenants generated overlapping land rights after Uganda became independent in 1962, and were the early roots of land conflict in the country. 35
  36. 36. II. Arabica and Robusta production Figure 5: Log of monthly Robusta prices and log of Arabica prices from 2002-2014 Figure 1: The log of Robusta prices and the log of Arabica prices are graphed against the CMC measure of time As shown in Figure 1, Arabica and Robusta prices share the same upward trend over time and have experienced roughly the same monthly price volatility from 2002-2014—the correlation coefficient between Robusta and Arabica is greater than 0.8. Robusta accounts for 94% of the coffee output of Uganda and Arabica accounts for only 6% (Ahmed, 2012). The estimated total area under coffee production is 272,000 hectares (ha), with Robusta cultivated on 250,000 ha and Arabica on only 22,000 ha (Ahmed, 2012). As shown on Figure 2 below, Arabica is grown around Mount Elgon in the east and in the mountainous ranges in West Nile and Mount Rwenzori in southwest Uganda (Kraybill and Kidoido, 2009). Finally, the large difference in production and exports discussed in Section III demonstrates that Robusta exports 3456 1200 1250 1300 1350 1400 CMC Code logarabica logrobusta 36
  37. 37. represent a significantly larger source of income from global markets, and explains why this study uses Robusta in the model. Figure 6: This map showing the Ugandan districts that have suitable land for Arabica cultivation III. Additional Results Table 6 shows additional results on the effects of coffee prices the extensive margin: I measure the impact of income on the outbreak of violent conflict. The violent conflict dummy variable isolates battles (potentially over land) from political conflict in the coded form of “Riots/Protests” and “Establishment of a Base or Headquarters.” While the results in Table 6 also support the rapacity effect, they are extremely similar to those in Table 2, since non-violent forms of conflict are very rare in this dataset. Therefore, there is not a differential impact on violent conflict relative to the statistically significant, positive impact on the conflict dummy. 37
  38. 38. Table 5: Violent Conflict Outbreak Results Dependent variable: violentconflict OLS Fixed Effects Fixed Effects Fixed Effects Fixed Effects Fixed Effects (1) (2) (3) (4) (5) (6) Percent Coffee Plots -0.050*** (0.016) Log Robusta -0.140*** -0.140*** (0.044) (0.044) Log Robusta x Percent Coffee Plots 0.011*** 0.011*** 0.011*** 0.004* (0.003) (0.003) (0.003) (0.002) Positive Shock x Percent Coffee Plots 0.006*** (0.002) Negative Shock x Percent Coffee Plots -0.010*** (0.003) District Fixed Effects N Y Y Y Y Y CMC Fixed Effects N N Y Y Y Y Regional Time Trends N N N Y N N Number of Observations 8,736 8,736 8,736 8,736 8,736 8,736 Number of Districts 56.000 56.000 56.000 56.000 56.000 note: *** p<0.01, ** p<0.05, * p<0.1

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