Effects of Refugee Inflows on Host Communities: the case of Somalia

Effects of Refugee Inflows

on Host Communities:

The case of Somalia

Index • Abstract p. 4 • Famine definition p.5 • Literature review p.6-12 • Somalia Crisis p.13-14 • Brief history of last year’s p.15-16 • IDP – Internally Displaced Persons p.17 • Patterns of Displacement p.18 • Famine Prevention and Drought Response Plan p.19-20 • Literature on the impact of humanitarian crises on local economies p.21-23 • Theoretical Framework p.24-26 • Current Situation p.27-33 • Possible policies to build resilience in Somalia p.34-36 • Data and estimation methods p.37-38 • Data explanation p.39-47 • Estimation results p.48-58 • Conclusions p.59 • Bibliography p.60-65

Abstract Main purpose of this research is to evaluate the impact of the 2017 Somalia crisis on local markets. I will concentrate my analysis on the city of Baidoa. The question I will try to answer is: can we quantify the impact of the 2016-2017 crisis in economic terms? Has the arrival of displaced people and poor rainfalls impacted on the prices in local markets? First I will review theories about famines and theories about the impact of humanitarian crises on local economies. Then I will analyse monthly data of food prices in Baidoa to try to give an answer to my question. Finally I will try to give suggestions on possible ways to end this crisis and to help people build resilience. Somalia is a protracted crisis (1991) that is caused by conflict, poor governance and natural disasters. Drought and floods are problems that the population have to face nearly each year. The 2016-2017 drought has been one of the most long and intense of the last years. A rapid response has not prevented population displacement caused by conflict and drought but has made possible for the displaced population to stay much nearer to their houses. It follows that appropriate actions done for the reconstruction of rural resilience would have much more chances to contribute at a normalization of the living conditions that is more rapid

Famine Definition According to the Integrated Phase Classification Version 2.0 (IPC 2.0) Famine (IPC Phase 5) is declared when the following criteria are true: 1- At least one in five households faces an extreme lack of food 2- More than 30 percent of the population is suffering from acute malnutrition (wasting) 3- At least two people out of every 10000 are dying each day. [1] Somalia has endured two seasons of poor rainfall, increased by the effects of El Niño. Food insecurity is rising. Politically Somalia is at a landmark with a new Parliament and recent presidential elections. The Country continues to face militant attacks from al-Shabab group that is linked to al-Qaida. [2]

Literature Review Famines can’t be explained by a single factor. Every case has to be studied separately. In the attempt to explain how famine happens economists have applied different approaches that will be analysed here. Main contributions can be divided in three groups each one from three different disciplinary perspectives. 1- Demographic The first contributor to the theory of famine from a demographic perspective was Thomas Malthus. In his famous essay ‘Essay on the Principle of Population (1789) he demonstrated that population cannot grow indefinitely in a world characterized by fixed natural resources. Eventually famine would act as a natural check on population growth equilibrating demand for food and food supply. “I think I may fairly make to postulata. First, That food is necessary to the existence of man. Secondly, that the passion between the sexes is necessary and will remain nearly in its present state.” “Assuming then my postulata as granted, I say, that the power of population is indefinitely greater than the power in the earth to produce subsistence for man.

This implies a strong and constantly operating check on population from the difficulty of subsistence. This difficulty must fall somewhere and must necessarily be severely felt by a large portion of mankind”. [3] Other malthusianists were Paddock and Paddock who wrote ‘Famine – 1975!’ (1967), and Hardin’s ‘Lifeboat Ethics: The Case against helping the Poor’ (1977).

“If poor countries received no food from the outside, the rate of their population growth would be periodically checked by crop failures and famines. But if they can always draw on a world food bank in time of need, their population can continue to grow unchecked, and so will their "need" for aid. In the short run, a world food bank may diminish that need, but in

the long run it actually increases the need without limit.”

“Without some system of worldwide food sharing, the proportion of people in the rich and poor nations might eventually stabilize. The overpopulated poor countries would decrease in numbers, while the rich countries that had room for more people would increase.” [4]

Lester Brown argued that productivity gains from agricultural intensification are lowering food production while food demand continues to increase [5]. Rising demand and stagnating production can lead the world to an era of food scarcity [6].

Ester Boserup in her ‘The impact of scarcity and plenty on development’ described a counter-argument against Malthusian theories for Sub-Saharan Africa. “Excessively low population density increase vulnerability to famine by inhibiting investment in basic economic infrastructure and agricultural technologies” [7]. A possible escape from the Malthusian trap would come from technological improvement in agriculture increasing productivity of land and labor. The necessary condition is that this change is fast; otherwise population growth would increase more than

proportionally. Her critics to neo-Malthusian theories are: 1) “technological progress in agriculture would not result in further population growth in cases where factors other than insufficient food supply were the effective restraints on population”, 2) the malnourished were always the poor, and they would sometimes lose more than they gained by changes in agricultural technology, at least in the short run”, 3) “The Malthusian theory overlooks the effects of population increase on technological change” [8, p.384]. 2- Economics There are two main theories regarding the causes of famine that attribute them to economic factors.

The first sees famine as the result of imperfect markets, either because they are weak or because of prices that go up for speculative and precautionary hoarding. Among the most important contributors there are Seaman and Holt (1980) that discovered that during the Wollo famine food prices rises where exported from the famine epicenter when drought- stricken population migrated from the epicenter [11]. Von Braun et al. (1998) demonstrated econometrically that ‘segmentation was prevalent in many food markets in Ethiopia during the famine years’ of the 1980’s [12]. Finally Ravallion (1987) demonstrated that alarmist

argumentation to the demand side, or ‘entitlement collapse’. For Sen the cause is to be found in the inability of people to command enough food for subsistence, irrespective to the food available at local or national level. The following are extracted from his work and sum up his theories.

“Starvation is the characteristic of some people not having enough food to eat. It is not the characteristic of these being not enough food to eat. While the latter can be a cause of the former, it is but one of many possible causes. Whether and how starvation relates to food supply is a matter for further investigation.”

“Food supplies statements say things about a commodity (or a group of commodities) considered on its own. Starvation statements are about the relationship of persons to the commodity (or that commodity group).” “Ownership relations are one kind of entitlement relations”. “Entitlements relations accepted in a private ownership market economy typically include the following, among others: (1) Trade-based entitlement: one is entitled to own what one obtains by trading something one owns with a willing party (2) Production- based entitlements: one is entitled to own what one gets by arranging production using one’s owned resources or resources hired from willing parties meeting the agreed conditions of trade

(3) Own-labor entitlement: one is entitled to one’s own labor power, and thus to the trade-based and production- based entitlements related to one’s labor power

(4) Inheritance and transfer entitlement: one is entitled to own what is willing given to one by another who legitimately owns it, possibility to take affect after the latter’s death (if so specified by him)” [14, p.1-2].

“In a market economy, a person can exchange what he owns for another collection of commodities. He can do this exchange either through trading, or through production, or through a combination of the two.” “The set of all the alternative bundles of commodities that he can acquire in exchange for what he owns may be called the exchange entitlement of what he owns.” [14, p.3] “A person’s ability to award starvation will depend both on his ownership and on the exchange entitlement mapping that he faces.”

“More importantly, his exchange entitlement may worsen for reasons other than a general decline of food supply” [14, p.4].

“In so far as food supply itself has any influence on the prevalence of starvation, that influence is seen as working through the entitlement relations” [14, p.5].

“In analyzing starvation in general, it is important to make clear distinctions between three different issues: (1) lowness of the typical level of food consumption (2) declining trend of food consumption and (3) sudden collapse of the level of food consumption. Famine is chiefly a problem of the third kind, and while it can, obviously, be helped by the first two features, if often does not work that way” [14, p.40-41].

“The entitlement approach to starvation and famines concentrates on the ability of people to command food through the legal means available in the society, including the use of production possibilities, trade

means of commanding food that are legitimized by the legal system in operation in that society.”

“ Ownership of food is one of the most primitive property rights, and in each society there are rules governing this right. The entitlement approach concentrates on each person’s entitlements to commodity bundles including food, and views starvation as resulting from a failure to be entitled to a bundle with enough food” [14, p.45].

“If some people had to starve, then clearly, they didn’t have enough food, but the question is: why didn’t they have food? What allows one group rather than another to get hold of the food that is there? These questions lead to the entitlement approach, which has been explained in this monograph” [14, p.154].

“A person’s ability to command food indeed, to command any commodity he wishes to acquire or retain, depends on the entitlement relations that given possession and use in that society. It depends on what he owns, what exchange possibilities are offered to him, what is given to him free, and what is taken away from him” [14, p.155]. 3- Political Keen in his work ‘The Benefits of Famine: A Political Economy of Famine and Relief in South-western Sudan, 1983-1989’ said that famine is not the consequence of economic but political powerlessness. As he stated the cause is to be found in ‘their near-total lack of rights or political muscle

within the institutions of the … state’ [15, p.211].

He then goes further saying: ‘The real roots of famine may lie less in a lack of purchasing power within the market than in a lack of lobbying power within national institutions’ [15, p.213].

Sen and Drèze wrote ‘Hunger and Public Action’ in 1989 [16]. In this work they turned their attention to the political conditions that may bring a country or region to famine. The two key arguments were a disseminate information about the food crises and free elections that would ensure the government’s accountability to its electorate. De Waal in his 1997 work ‘Famine that kills’ argues that the success of India in preventing famines was for his governmental institutions [17, p.7]. Today the state of the debate can be divided into two main theoretical ideas. The dominant one views famine as a natural disaster or economic crisis and that can be prevented by policy intervention, early warning and relief interventions. The second views famine as the result of local power struggles, state repression or afflicted population views. The main contributors to the first are Sen, Drèze and Ravallion while for the second are de Waal, Duffield and Keen. In my opinion each case is different and has to be analysed separately. This is why I want to concentrate on Somalia’s 2017 famine.

2017 Somalia Crisis Currently the world is facing four famines: in South Sudan, Somalia, Yemen and northeast Nigeria. Globally 108 million people were reported to face food insecurity in 2016; caused by conflict, record-high food prices and abnormal weather caused by El Niño. Severe drought conditions and insecurity in 2016 lead Somalia’s President Mohamed Abdullah Mohamed to declare it a national disaster on the 28th _{of February 2017. The latest} analysis confirmed an estimation of 2.9 million classified as severely food insecure in February 2017 with risk of famine. The outlook points out a worsening of the overall situation throughout the year. Conflict caused widespread displacement (internal and cross-boarder) causing 2.1 million refugees in Somalia alone. El Nino manifested itself in drought conditions damaging agricultural live hoods and triggering food insecurity. Among the most stricken there is Somalia and projections indicate an increase in the severity of food insecurity in the region [19, p.4]. The last publication from FAO on Somalia [21] shows a high risk of famine. Risk of famine in Somalia in 2017 increases due to current food assistance programmes interrupted and higher prices decrease food access. Due to the prolonged drought 3.1 million people, 25% of the population are expected to be in crisis (IPC Phase 3) or Emergency (IPC Phase 4) through December. Poor Gu (March- June) rains; pests’ infestations and reduced cultivations have lead to a reduction of -37% in crop production less than the ten-year average [21, p.8]. Livestock is reduced dramatically due to the prolonged drought and need at least two years of rains to recover. Drought has caused massive displacement of the population. According to the latest Food Security and Nutrition Analysis Unit (FSNAU) in August 2017 over 700000 people were displaced in the

first half of 2017. Acute watery diarrhoea and cholera outbreak that peaked in April 2017 contributed to increase malnutrition and mortality. According to the WHO and to the Federal Ministry of Health the worst measles outbreak, with nearly 15000 cases, was recorded by the end of June 2017. Deyr (October- December) rains are expected to be at normal to below normal levels. Combined with warmer than normal temperatures this is expected to lead to faster depletion of pasture and water causing stress on crops during the growing season. The recommended early actions for the period October- December 2017 are: 1) ‘Increase cash-based assistance to increase food access’ 2) ‘Urgent provision of agricultural inputs to enable farmers and agro-pastoralists to plant for the Deyr 2017 cropping season’ 3) ‘Provision of emergency livestock support to keep animals alive and productive, including vaccination, treatment, fodder/feed supplements, water tanks and water trucking as necessary’ 4) ‘Work with authorities to plan and prepare for a potential outbreak of the fall armyworm. While an outbreak was not officially declared in Somalia, considerable risk remains given its presence in neighbouring countries’ [21, p.8]. The last Situation Report on Somalia shows that Gu harvest cereal production had decreased 50% due to severe drought and production in the secondary Deyr season was down 70% [23]. Over half of the

Brief history of last years The country is fragile and impoverished, with half of the population living below the poverty line (51.6%). Domestic revenue is insufficient to allow the government to deliver services and the country remains dependent on international assistance. As of March 2017 over half of Somalia population (6.2 million) are food insecure and need humanitarian assistance. Following the collapse of the authoritarian socialist government in January 1991 Somalia descended into cycles of clan-based internal conflict and displacement. The impact of recurrent drought combined with other natural hazards on food and livehood lead two areas of the county to famine, the first from 1991 to 1992, and the second in 2011. Restriction on trade and freedom of movement imposed by Al-Shabaab famine spread across all regions of the south in 2011, and estimated 260000 people died. An internationally backed federal government was installed in 2012 and a compact was made with the international community. Fighting continued between groups and between the militant group Al-Shabaab and the country’s armed forces, backed by the African Union Mission in Somalia. A new President was elected in February 2017 after the first direct elections in 1960, amid the international concerns about violence perpetuated by Al-Shabaab in southern Somalia. In addition to violence also the imposition of taxes on households, farms and livestock by Al-Shabaab caused displacements. Two consecutive droughts deteriorated food security and the levels of nutrition and health towards the end of 2016. The UN issued warning of potential famine by February 2017, with nearly the half of the population (around 6 million) facing acute food insecurity. Thousands of families depending on livestock and agriculture have been forced to abandon their homes and normal

migration patterns to seek grazing land, water, work or life-saving assistance elsewhere. Floods and storms also impact exposed and vulnerable population almost every year accounting over 224000 new displacements between 2008 and 2016. Somalia precarious reliance on rainfall makes the country vulnerable to climate change. Voluntarily returning Somali refugees have added to the internally displaced population. More than 24600 Somalis were repatriated from Kenya between January and October 2016 under an agreement between UN Refugee Agency (UNHCR) and the Kenyan and Somali governments. IDP’s insecure tenure makes them vulnerable to secondary displacement through forced evictions when government or private landlords seek to reclaims their property. Nearly 130000 cased of forced evictions of IDP’s were reported in Mogadishu, Kismayo, Baidoa and Luuq in 2015. [37]

IDP- Internally Displaced Persons ‘Internally displaced persons (IDPs) have not crossed a border to find safety. Unlike refugees, they are on the run at home’ [28]. IDP stays within the borders of their country and remain under the protection of their government, even if it is the reason for their displacement. As a result, they are among the most vulnerable in the world. ‘For the purposes of these Principles, internally displaced persons are persons or groups of persons who have been forced or obliged to flee or to leave their homes or places of habitual residence, in particular as a result of or in order to avoid the effects of armed conflict, situations of generalized violence, violations of human rights or natural or human-made disasters, and who have not crossed an internationally recognized state border’ [29].

Patterns of Displacement Around the half of the population are nomadic pastoralists. Protracted displacements tend to be urban. In 2015 estimated 85% of the 80000 households living in 486 settlements were thought to be IDPs. As of April 2016, the largest concentration of IDPs was in Mogadishu, followed by other urban centres, mostly driven by conflict, drought and eviction from their former homes or shelters. IDP settlements transform into urban slums such as the one in Hargeisa. IDPs in Puntland were also concentrated in the main cities with most originating from southern and central Somalia and some local displaced for drought conditions. As of late November 2016, the central area of rural eastern Somaliland was largely deserted. Most of the displaced are children. Between November 2016 and April 2017 the number of displaced were around 615000. In the same period new conflict related displacements have continued, including 90000 displaced in Gaalkacyo in November 2016, 29000 in Lower Shabelle, 27500 in Qandala and 5000 in Bakool and Hiraan in December 2016. Most refugees repatriated in 2016 returned to Baidoa, Kismayo, Luuq and Mogadishu continuing to live in internal displacement. The highest poverty levels in Somalia are found among IDPs settlements (71%). [37]

Famine Prevention and Drought Response Plan [27] Somalia is actually free of famine but the risk is elevated. Between February and May 2017 the number of acutely food insecure rose from 500000 rural people to 6.7 million. 9 out of 10 on the brink of famine (IPC 4) live in rural areas. Livelihoods are the greatest defence against famine and the opportunity to recover quickly from it. Deyr rains can be the turning point since they are estimated to be the first good rains in the last two years. They can produce the harvest that could help Somalia end the crisis. Farmers need the seeds to grow Deyr crops in October- December. Late 2017 present risks of floods and a 45% chance to develop El Niño [27, p.2]. Insufficient rain and water availability reduced crop production and caused livestock losses. Food and water prices rose over farmer’s means that became more dependants on market purchases. Farmers in the south faced yield reductions of 50% in mid 2016 and 70% at yearend. Pastoralists in the north endured poor rains and livestock losses for up to three years. Families become destitute and indebted, a famine alert was issued in early 2017 and AWD/Cholera began to spread. After two critically low harvests in 2016 farmers don’t have food or seed stocks left. Poor pastoral and agro pastoral families have lost up to 60% of their herd. People are leaving rural areas to where they think they can find food and water supplies. According to UNHCR 739000 people have been displaced due to drought since November 2016. Once the livelihood collapses they become displaced and as time passes it become more difficult for them to return [27, p.3]. Poor Gu rains (April- June 2017) lead to another poor harvest. This year Deyr rains (October- December) can be a turning point since the forecast

says that rains will be between average and above average. Efforts have to be concentrated in help farming families in rural areas and plant well in October 2017. Many displaced people, for example in Baidoa, are close to their homes and they request seeds and assistance to help them return to farming. Livestock looses have been high among poor families, averaging 40-60 percent in the north and centre and 20-40 percent in the south. Above average Deyr rain can cause floods in some areas if floods are not managed properly. There is a 45% probability of an El Niño event with above average rains that causes floods [27, p.4]. FAO intervention plan is to 1) Keep Somalia Famine-free, through cash based interventions, Cash+ transfers and livestock support 2) Support early IDP returns and their communities, restoring productive livelihoods 3) Resist to new shocks, building early warning and flood preparedness work and protecting crops against plant pests [27, p.5]. The first point comprehends cash relief interventions, especially in rural areas where families lack food and income to buy food due to expensive crop failure and livestock losses; FAO Cash+ programme combines unconditional cash transfers and emergency livelihood support, families receive monthly cash transfers and emergency live hood support; emergency livestock support, especially in the areas where pastoralism is

Literature on the impact of humanitarian crises on local economies Williamson and Hatton’s [48] try to respond to the question on the causes of refugee’s displacement asking if they are mainly political or economic. How can wars, political crises and economic conditions explain the number of asylum seekers? Their literature review reveals considerable work on the determinants of population displacement as well as how policies have affected the direction of human flights. Baez [49] studied the short and long run effects of hosting refugees on the outcomes of local children. He concentrates on the period between 1993 and 1994 on the genocides of Burundi and Rwanda when over one million people were killed and 500000 moved to Tanzania. He shows a negative impact on health outcomes of residents living close to refugee camps. Among those who studied the impact of refugee camps on prices in nearby markets there are the following authors. Borton, Brusset and Hallam [50] discuss prices spikes and tell that local population suffer from this. Beth Elise Whitaker [51] focuses on changing opportunities of host communities. Her conclusions are that different strategies and structures allowed some hosts to benefit while others become worse off. In the end Tanzanian hosts developed ways to cope with negative aspects of the refugee presence while taking advantage of positive opportunities [51, p.3]. Landau compare prices in markets near refugee’s camps with one in the central part of country and find little evidence of any impact on prices [52]. Barrett [53] review the effects of food aid on local prices and the empirical results are mixed, with much of the research focused on work programs.

Maxwell and Singer [54] review the evidence on the impact of food aid on growth and its associated factors identifying a set of guiding principles for maximizing the effectiveness of food aid. Singer, Wood and Jennings [55] give a clear insight into key issues in food aid, presenting an assessment on the uses and misuses and relating these to the complexity of the international economy. Ruttan [56] brings together essays and commentaries on food aid policy focusing on the needs, problems and the future. The book attempt is to understand the impact of development assistance and the economic and political sources of development assistance policy. Insenman and Singer [57] analyses the effects of food aid on agricultural production. To evaluate the net effect of food aid on domestic food production he considers its effects on the price of food and on government policies. Dercon and Krishnan [58] look into the extent to which food aid helps to smooth consumption by reducing the impact of negative shocks, taking into account informal risk-sharing arrangements. Using data for Ethiopia the authors find that food aid programs contribute to better consumption outcomes, largely via intra-village risk sharing. Abdulai, Barrett and Hoddinott [59] using household data from Ethiopia demonstrate that no empirical support remains for the hypothesis that food aid crates disincentive effects among recipient households. They find

Population increase can also change local prices, through increased demand for goods and increased supply of inexpensive labour. Borjas [61] studies the U.S. Census data for 1980 and it reveals that immigrants tend to be substitutes for some labour market groups and complement for others. Card [62], using data from the Current Population Survey, describes the effect of the Mariel Boatlift of 1980 on the Miami labour market showing positive results due to the labour market ability to rapidly absorb immigrants. The Mariel influx had no effect on the wages or unemployment rates. Cortes [63] exploits the large variation across U.S. cities and through time to estimate the causal effect of immigration on prices on nontrade goods and services. Lach [64] found that the movement of refugees from the Soviet Union to Israel in 1990s and showed that immigration can have a moderating effect on inflation through its direct effect on product markets, and not only by increasing the supply of labour. Alix-Garcia [65] uses variations in refugee population and food aid over time to examine the impact of proximity to refugee camps and aid on prices of Tanzanian agricultural goods. Her estimates show increases in the prices of most goods in markets closer to refugee camps as a result of the refugee inflows, though the effect is much larger for Rwandan refugees than for Burundian refugees.

Theoretical Framework This section presents a simple framework for analysing the local price effects of population displacement and consequent flows of aid. It is taken from the work done by Alix-Garcia and Saah [65]. Large inflow of refugees and aid implies both supply side and demand side effects on the market. On the supply side food aid increases aid-related goods and may put downward pressure on prices if the food aid is imported and upward pressure if it is provided locally. The inflow of refugees may also depress wages, which may result in falling prices where labour is an important agricultural input. Population increase means an increase in demand for all goods and it can lead to a change in the prices of tradable goods only when trade with areas outside affected regions is limited. A household has a concave utility function depending on their consumption of aid goods, 𝑥_!, and non-aid goods, 𝑥_!. Budget constraint is the sum of spending on consumption equal to income: 𝑝_!𝑥_! + 𝑝_!𝑥_! = 𝑚 Refugee population income, 𝑚_!, is assumed to be different from the host population, 𝑚_!, with 𝑚_! ≠ 𝑚_!. Utility maximization of

Concavity means that demand functions are decreasing in own price and increasing in the price of the other good and in income: 𝜕𝑥_!!/𝜕𝑝_! < 0 𝜕𝑥_!!/𝜕𝑝_! ≥ 0 𝜕𝑥_!!_/𝜕𝑚 ! > 0 R denotes the total number of refugee households. H is the total number of host households. Market demand for goods yields 𝐻𝑥_!"! 𝑝_!, 𝑝_!, 𝑚_! + 𝑅𝑥_!"! 𝑝_!, 𝑝_!, 𝑚_! Refugee and host populations may participate as labourers in the production of all goods, whose main input is labour. A concave production function will yield supply functions as: 𝑥_!!(𝑝_!, 𝑤, 𝛼_!) 𝑊ℎ𝑒𝑟𝑒 𝛼_! is a parameter indicating the productivity of labour; 𝑝_! is the price of good 𝑖 𝜖 𝑎, 𝑛; w is the wage. Supply function is increasing in his own price and decreasing in the wage 𝜕𝑥_!!/𝜕𝑝_! > 0 𝜕𝑥_!!/𝜕𝑤 < 0 Aggregate supply is the sum of supply for P, individual producers. It is assumed that, in the short run, P does not depend directly on R. Aid depends on the number of refugees. Imported aid is 𝑎_! 𝑅 and increases in R. Quantity of aid purchased locally 𝑎_! 𝑅 and affects the market on the demand size. Equilibrium in aid related markets are determined as 𝐻𝑥_!"! 𝑝_!, 𝑝_!, 𝑚_! + 𝐻𝑥_!"! 𝑝_!, 𝑝_!, 𝑚_! + 𝑎_! 𝑅 = 𝑝𝑥_!! _𝑝 !, 𝑤; 𝛼! + 𝑎!(𝑅) Differentiating by price and the number of refugees gives:

𝜕𝑝_! 𝜕𝑅 = 𝜕𝑎_! 𝜕𝑅 −𝜕𝑎𝜕𝑅 − 𝑥! !"! 𝐻𝜕𝑥_𝜕𝑝!!! ! + 𝑅 𝜕𝑥_!"! 𝜕𝑝_! − 𝑃𝜕𝑥! ! 𝜕𝑝_! Denominator is always negative. Domestically produced aid and foreign-supplied aid move in opposite directions. Additional demand from refugees puts upward pressure on prices in the same way that domestically produced food aid might. If foreign aid effect 𝜕𝑎_!/𝜕𝑅 exceeds the other two effects the price will decrease. Labour is demanded in the production of both aid-related and non-aid goods. Assuming substitutability between refugee and host labour, and a concave production function, the increase in the labour supply caused by the refugees is easily shown to depress the wage. Price of aid-related goods will be affected by both the increase in population caused by the refugee inflow and the availability of food aid. Net effect will be the sum of these two and these effects move in opposite directions when food aid is foreign supplied and the same direction when food aid is purchased locally. Non-aid goods are likely to experience price increases because there is no mitigating effect from foreign-supplied aid.

Current Situation As we can see from figure 1, with data extracted from the UNHCR Population Statistics Reference Database, the number of IDPs has increased in the last years. 0 300000 600000 900000 1200000 1500000 1800000

IDPs in Somalia

From figure 2, taken from the UNHCR website, we can see that the majority of IDPs are in the South Central Region. INDIAN OCEAN Red Sea Gulf of Aden BURUNDI DEMOCRATIC REP. OF THE CONGO DJIBOUTI ERITREA ETHIOPIA KENYA RWANDA SAUDI ARABIA SOMALIA UNITED REPUBLIC OF TANZANIA UGANDA YEMEN SUDAN SOUTH SUDAN 13,043 2,287 252,385 287,397 35,373 256,283 AWDAL WOQOOYI GALBEED SANAAG _BARI SOOL TOGDHEER NUGAL MUDUG GALGADUUD HIRAAN BAKOOL MIDDLE SHABELLE BANADIR LOWER SHABELLE BAY GEDO MIDDLE JUBA LOWER JUBA Lake Albert Lake Turkana Lake Kivu Lake Tanganyika Lake Edward Lake Victoria

showing host countries with more than 1,000 Somalis | as of 30 September 2017

161,000 IDPs

152,000 IDPs

1.25M IDPsSOUTH CENTRAL

PUNTLAND SOMALILAND

$

REFUGEES AND ASYLUM-SEEKERS

INTERNALLY DISPLACED IN SOMALIA

2.41

TOTAL DISPLACED SOMALIS

1.56

846,968

MILLION

50km East, Horn of Africa and Yemen

Displacement of Somalis: Refugees, asylum-seekers and IDPs

WHERE ARE THEY?

MAIN HOST OPERATIONS

hosting > 20,000 Somalis

REGIONS OF ORIGIN OF SOMALIS REGIONS OF ORIGIN OF SOMALIS

25% 23% 17% 10% 10% 3% 3% 2% 1% 1% 1% 1% 1% 1% 1% Banadir Lower Juba Gedo Bay Middle Juba Lower Shabelle Bakool Woqooyi Galbeed Hiraan Middle Shabelle Bari Awdal Galgadud Nugal Other regions 5,900 8,900 700 6,200 1,600 1,500 4,300 2,800 4,400 8,300 15,100 7,200 59,600 98,100 AWDAL WOQOOYI GALBEED SANAAG BARI SOOL TOGDHEER NUGAL MUDUG GALGADUUD HIRAAN BAKOOL MIDDLE SHABELLE BAY GEDO ETHIOPIA DJIBOUTI *

* estimated IDP population at end-2016

33% 31%

Kenya | Dadaab Ethiopia | Dollo Ado

* * * ** 34% 30% 30% 4.2% 1.5% 0.3% Kenya Yemen Ethiopia Uganda Djibouti Eritrea

From figure 3, taken from FSNAU Food Security and Nutrition Analysis Unit-Somalia for the month of October 2017, we can see that the majority of hosted IDPs are in the Bay region and especially in the city of Baidoa.

Figure 4 shows Maize and Sorghum prices monthly in Baidoa from 1995 until today. Data was collected from WFP/INTERFAIS. 0 2000 4000 6000 8000 10000 12000 14000 16000 18000 20000 7/ 31/ 1995 7/ 31/ 1996 5/ 31/ 1997 3/ 31/ 1998 3/ 31/ 1999 3/ 31/ 2000 12/ 31/ 2000 9/ 30/ 2001 6/ 30/ 2002 3/ 31/ 2003 2/ 29/ 2004 12/ 31/ 2004 11/ 30/ 2005 9/ 30/ 2006 10/ 31/ 2007 7/ 31/ 2008 5/ 31/ 2009 2/ 28/ 2010 11/ 30/ 2010 8/ 31/ 2011 5/ 31/ 2012 2/ 28/ 2013 11/ 30/ 2013 8/ 31/ 2014 5/ 31/ 2015 2/ 29/ 2016 11/ 30/ 2016 8/ 31/ 2017

Price in Somali Shilling for a kg of

Maize or Sorghum in Baidoa

Figure 5, taken from the OCHA web site shows the location of IDPs settlements in Baidoa.

The Baidoa IDP settlement assessment report was conducted between 3rd and the 18th _{of April 2017. The report showed that 72% of IDP households} are caring for children under 5 years old. Of the households with children (95%) 4% reported to accommodate unaccompanied or separated children. More than 50% of the assessed households had a poor food consumption score. Only 2% of the households reported that the market was within a walking distance, consequently most households find it difficult to access the food markets due to the distance from the IDP settlements. 31% of households reported an increase in the amount spent on food and 42% a decrease, which could denote declining resources to purchase food items. The majority (51%) reported a decrease in the quality of food consumed in the last month. Only 21% of the households in the assessed settlements had accessed any nutrition services in the past three months. Of the assessed households, a considerable number reported to live in emergency (57%) or temporary shelters (27%). Several seasons of consecutive poor and failed rains had effects on pastoral and agro-pastoral communities, forcing them to travel vast distances to find water and grazing land. Distress migration has began on large scale with rural populations migrating towards urban centres in search of relief. Baidoa (Baydhabo) is a strategically town situated in south-central

displaced persons (IDPs) as a result of drought and conflict as well as loss of livehood. Baidoa is home to an estimated 168 IDP settlements, most of them in the town. An approximated 7000 households have moved to Baidoa since March 2017 resulting in loss of assets and sources of livelihood, livestock and land, due to displacement. In IDP people have little access to stable employment and food insecurity and water shortages continue to exacerbate the IDP situation. 99% of settlements inhabitants have mainly arrived from Bakool (20%), Lower Shebelle (1%), Middle Juba (1%), Gedo (2%), Banadir (1%), and other parts of Bay region (74%). IDPs’ main reason for leaving their previous area of long-term residence was drought (60%), conflict (10%) and loss of livehood (8%). When asked why they chose to come in their present location 74% of the IDP households reported availability of aid as the main reason. Other reasons were search for labour and income (30%), lack of conflict in the destination location (21%) and presence of family and friends in the destination locations (4%). [68]

Image 6 shows the area of origin of IDPs. [68]

Possible policies to build resilience in Somalia Insufficient rain and water availability severely reduced food production and caused livestock losses. Food and water prices rose beyond the means of farmers, pastoralists and wage labourers who lost income and became more dependent on market purchases. Farmers in the south faced yield reductions of 50% in mid-2016 and 70% at yearend, while pastoralists in the north endured poor rains and livestock losses for up to three years. A famine alert was issued in early 2017 and AWD/Cholera began to spread. People have lost the ability to afford food on their own and cannot recover from 1-3 years of assets losses and debt in few months time. Poor Gu rains (April-June 2017) will lead to another poor harvest. Deyr rains (October-December 2017) are forecasted above average and could produce the harvest that Somalia needs to end the current crisis and begin recovering. Humanitarian efforts must help farmers remain in rural areas and plant well in October 2017. Livestock looses have been very high among families, 60-70% in the north and 20-40% in the south. Floods are likely in late 2017. There is a 45% chance of El Nino event, which could bring heavier than expected rainfall in Somalia. Every child above age five has lived through a famine in Somalia (2011). Each young adult above age 25 lived through two famines (1991 and 2011). Conflict and violence have persisted for more than one quarter of a century. Somalia has 8.9 million hectares of cultivable land, the highest number of livestock per capita in the world, two large rivers (Shabelle and Juba) and two rainy seasons (Deyr and Gu), the longest coast in Africa (3300 km) and an enormous workforce in agriculture.

Image 7, taken from FAO resilience strategy 2017-2019, shows where main crop production is located. [69] [70] Possible measures are: 1. Cash-for-work and unconditional transfers. Families receive cash upon registration, equivalent of two weeks of paid labour. This enables families to immediately improve their food intake. Families unable to engage in work will receive unconditional cash. 2. Livelihood support, so that farmers and agro pastoralists can produce their own food. 3. Flood prevention and preparedness during the on going Deyr season and protecting crops against plant pests.

Data and estimation method I build the dataset with data taken from the Food Security and Nutrition Analysis Unit- Somalia website. This contains monthly data on local prices. I concentrated my analysis on the city of Baidoa since the crisis is located mostly in this city. The data I have used for my research are: the prices of Maize, Sorghum, Local Goat, Red Rice and Water in Somali Shilling per month; Monthly Rainfall in inches, NDVI vegetation index; arrivals and departures from the district each month; Wage Labour; Acute malnutrition cases (GAM); the Cost of a Minimum Basket (CMB). Monthly-normalized difference vegetation index (NDVI) measures vegetation vigour using satellite images and is a good proxy for agricultural productivity. Normalized difference vegetation index, which varies over time, controls for one of the main competing sources of agricultural price shocks: weather. The index measures vegetation “greenness” and this pick up variation in both temperature and rainfall. The equation is estimated using fixed effects ordinary least squares. IDP arrivals in Baidoa started in January 2015. The period taken in consideration for the analysis starts from January 2015 and ends in October 2017. I have done a correlation matrix to study and evaluate the association between the variables. To do this I have used the Pearson correlation method, which measures the linear dependence between two variables. If the p-value is <5% then the correlation between the two variables is significant. Results are shown in the table below.

Data explanation The current Deyr harvest (January 2017) is estimated to be 75% below average. Domestic staple food supply in 2017 is expected to be below average and similar to 2011 levels following consecutive below average harvest and in anticipation of a below average Gu harvest in July 2017. Maize and sorghum prices have increased rapidly in the Southern and Central surplus producing regions, where maize and sorghum are the dominant staple foods. Maize and Sorghum prices are likely to continue to increase in the Southern region, until the Gu harvest in July. Poor pasture and livestock availability are expected to result in poor livestock body conditions and relatively low prices (per head) in 2017. Somalia’s four staple foods are maize, sorghum, rice and wheat. While maize and sorghum are produced locally, rice and wheat are almost entirely imported. The two main harvest in Somalia are the January to March Deyr and from July to September Gu. The lean season peaks in June before the start of the Gu harvest. The Deyr harvest contributes at approximately 60% of annual coarse grain (maize and sorghum) production and the Gu contributes to the remaining 40%. On average, local sorghum and maize production cover domestic requirements, making Somalia self sufficient for maize and sorghum. Production is largely rain fed. The Southern region, that includes Baidoa, is the surplus-producing area of Somalia. Somalia produces insignificant amounts of rice and wheat and relies almost entirely on international markets to meet local requirements for the two goods. Domestic maize and sorghum production vary widely year to year but make approximately 40% of total national food supply on average while wheat and rice imports contribute the remaining 60%. Maize and Sorghum price trends are highly

dependent on the performance of domestic production. In contrast Somalia’s import dependence for rice and wheat has resulted in strong global price transmissions for those commodities to domestic prices. Global trends have a greater impact on rice and wheat retail prices in Somalia than local market trends. While local cereal prices follow strong seasonal trends, imported commodity prices do not. Markets are more integrated in Southern Somalia because of shorter distances between markets. The main sorghum and maize areas are Lower Shabelle, Bay and Bakool. Baidoa and Merka are the main markets and price leaders. The livestock sector accounts for 40% of Somalia GDP and provides food and income to over 60% of the country’s population. The on going January 2017 Deyr harvest is estimated to be 75% below the 2011-2015 average levels of Deyr production due to poor rainfalls. Cereal production is estimated to be significantly lower in the surplus-producing areas. Recent price trends have varied considerably by commodity and region. Imported rice and wheat prices remained relatively stable in 2016, maize and sorghum prices increased quickly in the last quarter of 2016 across the country, especially in Southern Somalia. In this region, December 2016 maize prices were above December 2015 and 2011-2015 average prices, and sorghum prices in the main producing Baidoa market were 68 and 88 percent above December 2015 and 2011-2015 average prices. Estimated imports of maize and sorghum from eastern Ethiopia to

Somalia, including Baidoa, sorghum prices are expected to reach more than 200% above 2012-2016 average prices and maize prices 100% above 2012-2016 average prices. Rice price levels are expected to act as a price ceiling for maize and sorghum prices, allowing them to only increase to a certain point. At the point that maize and sorghum reach rice prices, there is a high likelihood of cross-price substitution between sorghum, maize, rice and wheat flour. [71]

All of the graphs below are done with the data taken from the FSNAU database. As we can see from both the two graphs below Maize and Sorghum prices have increased during the last year. 4000 6000 8000 10000 12000 14000

Maize Prices

Maize Prices 2015 Maize Prices 2016 Maize Prices 2017

2000 4000 6000 8000 10000 12000

Sorghum Prices

The cost of a Minimum Basket has increased both in 2016 and 2017 respect to 2015. 0 500000 1000000 1500000 2000000 2500000 3000000

Cost of Minimum Basket

Monthly arrivals in Baidoa have increased in 2017 respect to the previous years. 0 10000 20000 30000 40000 50000 60000 70000 80000

Arrivals in Baidoa

Rainfalls in inches follow the two rainy seasons in Somalia. As we can see from the graph the quantity of rain has decreased drastically in 2016 and 2017. The Deyr harvest (January) and the Gu harvest (July) are in January and July respectively. As we can see from the data 2017 has seen a reduction in vegetation cover after poor rains in 2016. 0 50 100 150 200 250 300 350 400

Rainfall (inches)

Rainfall 2015 Rainfall 2016 Rainfall 2017

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7

Vegetation cover

The image below taken from FEWS NET shows us Somalia seasonal calendar.

Estimation results The first regression I have done was to study the possible effects of IDP arrivals, wage variations and NDVI vegetation index on the price of Maize. Maize is the dependent variable. Arrivals consider monthly arrivals in the city of Baidoa. NDVI is the monthly-normalized difference vegetation index. Wage_Labor contains monthly values of the wages in Baidoa. 𝑀𝑎𝑖𝑧𝑒 = 𝛿_!+ 𝛿_!𝐴𝑟𝑟𝑖𝑣𝑎𝑙𝑠_! + 𝛿_!𝑁𝐷𝑉𝐼_! + 𝛿_!𝑊𝑎𝑔𝑒_𝐿𝑎𝑏𝑜𝑟_! + 𝑢_! I choose these variables to study if they had an impact on Maize prices in Baidoa since the beginning of the crisis. Arrivals could explain part of the variation in Maize price since an increase of the city population due to Internal Displacement. NDVI can explain part of the variation since it considers monthly vegetation and event as drought or climatic events. Wage could consider monthly inflation. The F-test null hypothesis is that R squared is equal to zero. If it is true the model is not good, it doesn’t explain the variation in the dependent variable. The alternative hypothesis is that the R squared does not equal zero, or that the model has explanatory power. If the p-value is less than 0.1, 0.05 or 0.01 then we have respectively 90%, 95% or 99% of significance. Since in our case is essentially zero we can say that our

From the R squared we interpret the t values [Pr>t]. What we want to see is t-values being less than 0.1, 0.05, and 0,01. The null hypothesis in the case of t-test is that the coefficient of the variable equals zero. It is possible to reject the null hypothesis is one case and not in one other. We can reject null hypothesis in all cases except for NDVI. Arrivals and Wage_Labor have significant effects on Maize Prices. We are confident at the 90% level that they don’t equal zero and have significant impact on Maize Price. Coefficients of Arrivals and NDVI are positively correlated with Maize Price while Wage_Labor is negatively correlated to it.

I regressed the monthly Sorghum Price in Baidoa on monthly Arrivals, NDVI index and Wage Labor. 𝑆𝑜𝑟𝑔ℎ𝑢𝑚 = 𝛿_!+ 𝛿_!𝐴𝑟𝑟𝑖𝑣𝑎𝑙𝑠_! + 𝛿_!𝑁𝐷𝑉𝐼_! + 𝛿_!𝑊𝑎𝑔𝑒_𝐿𝑎𝑏𝑜𝑟_! + 𝑢_! As we can see from figure 7, the region for the production of Sorghum is located in the region of Bay, where Baidoa is the capital. This could explain the high value of R squared of our analysis. Wage_Labor has a negative impact also in this case while the other variables have positive impacts on the price of Sorghum. Standard error is the standard deviation for the coefficient, how much deviation there is in the prediction for that coefficient. The t-stat is the number of standard errors our coefficient is from zero. Coefficient over Standard Error gives the t-stat. P-values are significant at 99% confidence level for Arrivals, 90% for Wage_Labor while is not significant for NDVI. From the F-test we can say that we have 99% confidence that we can reject the null hypothesis and the regression model has explanatory power.

In this regression I study possible relations between the price of Local Goats and monthly Arrivals in the city of Baidoa, the NDVI index and the Labor Wage in Baidoa. 𝐿𝑜𝑐𝑎𝑙 𝐺𝑜𝑎𝑡𝑠 = 𝛿_! + 𝛿_!𝐴𝑟𝑟𝑖𝑣𝑎𝑙𝑠_! + 𝛿_!𝑁𝐷𝑉𝐼_! + 𝛿_!𝑊𝑎𝑔𝑒_𝐿𝑎𝑏𝑜𝑟_! + 𝑢_! Without analysis we could say that there is no or little relationship between the prices of Local Goats and the explanatory variables since is the North of the country that is mainly pastoralist as we can see from image 7. The regression results are in line with this. The sum of squares is the amount of variation we have. The model explains with 3 independent variables the 29% of the variation. The mean squares are the sum of squares divided by the respective degrees of freedom. The null hypothesis states that the coefficients are equal to zero. If we cannot reject it the model cannot explain the variation of the prices. The F-statistic can be calculated by dividing the mean squares of the model on the mean squares of the residuals. The 𝐹_!,!" value of 4.155 let us reject the null hypothesis. The p-value is less than the level of significance at 5% so at least one of the independent variables is significant. There is a negative relation between arrivals in Baidoa and an increase in the price of local goats. For every additional arrival in Baidoa, the expected price for one Goat decreases by 0.978 on average, holding all other variables constant. Standard errors give the average error term from the sample value. We can divide the coefficient by the standard error to get the t-statistic. The higher the t-statistic the more significant the variable is. In our case only Wage_Labor is significant.

The P-value, given the null hypothesis that is zero, gives the probability of these occurring just to random chance. The null hypothesis can be

rejected just for Wage_Labor at 95% of significance.

The price of Red Rice seems to don’t have relations to the variables. The p-value is high and from the t-statistics we can say that it doesn’t have statistical significance. 𝑅𝑒𝑑 𝑅𝑖𝑐𝑒 = 𝛿_! + 𝛿_!𝐴𝑟𝑟𝑖𝑣𝑎𝑙𝑠_! + 𝛿_!𝑁𝐷𝑉𝐼_! + 𝛿_!𝑊𝑎𝑔𝑒_𝐿𝑎𝑏𝑜𝑟_! + 𝑢_!

The price of Water considers also monthly Rainfalls in inches as an independent variable. 𝑊𝑎𝑡𝑒𝑟 = 𝛿_!+ 𝛿_!𝐴𝑟𝑟𝑖𝑣𝑎𝑙𝑠_! + 𝛿_!𝑁𝐷𝑉𝐼_! + +𝛿_!𝑅𝑎𝑖𝑛𝑓𝑎𝑙𝑙_! + 𝛿_!𝑊𝑎𝑔𝑒_𝐿𝑎𝑏𝑜𝑟_! + 𝑢_! This regression is statistically significant having a high R squared, a p-value near zero and t-statistics that reject the null hypothesis for the variables Arrivals and Rainfall.

In this case I regressed the monthly cases of global acute malnutrition cases in Baidoa (GAM) on Arrivals, Maize and Sorghum and the price of water. The results are statistically significant and the R squared is 67%. We can accept all the variables except for the price of Maize. 𝐺𝐴𝑀 = 𝛿_!+ 𝛿_!𝐴𝑟𝑟𝑖𝑣𝑎𝑙𝑠_! + 𝛿_!𝑀𝑎𝑖𝑧𝑒_! + 𝛿_!𝑆𝑜𝑟𝑔ℎ𝑢𝑚_! + 𝛿_!𝑊𝑎𝑡𝑒𝑟 + 𝑢_!

I studied the possible relations between the Cost of a minimum basket (CMB) and monthly Arrivals, prices of Maize and Sorghum and Water. The last two seem to explain the variation of the dependent variable. 𝐶𝑀𝐵 = 𝛿_!+ 𝛿_!𝐴𝑟𝑟𝑖𝑣𝑎𝑙𝑠_! + 𝛿_!𝑀𝑎𝑖𝑧𝑒_! + 𝛿_!𝑆𝑜𝑟𝑔ℎ𝑢𝑚_! + 𝛿_!𝑊𝑎𝑡𝑒𝑟 + 𝑢_!

Conclusion What appears from my analysis is that the prices of Maize and Sorghum were affected by monthly arrivals in Baidoa as well as by wages variations. The price for a local goat seems to be affected just by the variation in wages. On the contrary the price for red rice don’t have any or small relation to the independent variables. The price for water is highly dependant on rainfalls and monthly arrivals. Acute malnutrition cases are highly correlated to the increase in the price of sorghum, of water and to the monthly arrivals in Baidoa. Finally the cost of a minimum basket is highly dependant on the prices of sorghum and water. The 2016-2017 drought has been one of the most long and intense of the last years. Somalia has to face droughts and floods nearly each year. Appropriate action done for the reconstruction of rural resilience will give the contribution to the normalization of the living conditions.

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