avatar International Finance Corporation Finance, Insurance, And Real Estate


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    Esso Exploration & Production Chad Inc. Land Use Mitigation Action Plan Annual Individual Livelihood Restoration Report 2009 28 February 2010

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    List of Acronyms & terms used in this report HH Household. HHH Household Chief (Chef de Ménage(CdM) HHM Household Member. Include the CdM and all it dependents, regardless their age. Eligible Generic term to designate an individual that may be eligible to the EMP Resettlement Program. Potential Eligible Individual that may be eligible to the EMP Resettlement Program. Analysis must be completed. Note on Data In comparing data between tables, inconsistencies in numbers are due to the ever-evolving nature of the data (more fields belonging to M. Ngar….have been measured in another village; a “dependent” who, with further information, turns out to really belong to another HH). The overall messages delivered by the tables in this document remain the same, despite slight increases or decreases. The tables have been calculated at given points in time whereas the data continues to evolve.

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    EXECUTIVE SUMMARY The purpose of the Land Use Mitigation Action Plan (LUMAP) Annual Individual Report is to provide information on the number of people currently at risk because of Esso Exploration & Production Chad Inc (EEPCI) Oil Project (Project) land take impacts. It also follows the results of livelihood restoration activities completed, initiated or ongoing in the past year. The percent of individuals/households whose situations have been resolved or improved by the Project over the past year provides a measure of the efficacy of both the LUMAP tools and the livelihood restoration activities. LUMAP 2009 Livelihood Restoration Highlights The major accomplishment in 2009 was the recognition, thanks to sufficient data collection and analysis, that the social units at risk because of Project land acquisition are individuals and households rather than entire villages. This understanding allows the EMP socioeconomic group henceforth to identify correctly and specifically those who are impacted and to focus its actions where impact is most felt. A second major accomplishment for LUMAP was to have completed data gathering and to have identified people who are in fact Non-Viable but were not recognized as such at the time of earlier land acquisition. These individuals have either a) already been offered a resettlement option and been included in the Class of 2008-9 or b) will soon be offered resettlement benefits as part of the Class of 2010-11. At then end of 2009 LUMAP has caught up with problems created because of earlier land acquisition and slow restoration. The next task, the goal for 2010, is to embed these tools and techniques within the EMP socioeconomic process so that they become ongoing, sustainable activities and part of “business as usual”. Data in this 2009 report on livelihood restoration provides information not only on people at risk but others who were mistakenly identified as vulnerable. The monitoring of their advances is a demonstration of positive development impacts the Project may have had. Failures are failures in investment in training but the individual was never at risk. These individuals will not be tracked in the future. (If one of them is put at risk by future land acquisition of course the individual’s situation will be monitored). Viable people still in the midst of implementing their option will continue on to finish them out but they too will not be subsequently monitored for the prescribed 2 year period. This liberates EMP

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    socioeconomic resources to focus on helping non-viable households regain their livelihoods. A résumé of LUMAP livelihood restoration activities in 2009 in the nine areas targeted for action: 1. Data acquisition and analysis • Land use and Household Status surveys completed in 6 geographic villages, comprising 13 administrative units. • “Red Flag” or seemingly at risk individuals and their HH were also surveyed in 13 additional villages about their land use and HH status. • Results (data as of 16 Feb 2010): o Total population of 9 villages 100% surveyed = 8634 o Total number of HH surveyed in 100% villages = 1589 o Among Red Flags 118 HH were surveyed • The villages completely surveyed in 2009 were: Madjo Bero and Mouarom; Begada 1 & 2; Bela 1 & 2; Mbanga 1 & 2; Bero 1, 2, 3, 4 and Quartier Mududoigne. • Completed village survey data analysis for all but Bero. Bero and the Red Flag HH will be analyzed in 1Q 2010. • The numerically precise data from the Village Surveys showed that the impact of land acquisition on villages and households (HH) was less than earlier, less reliable data had implied. • 4 villages1 have given up enough land permanently or are still waiting for temporary land to be returned to qualify them as highly impacted by LUMAP criteria. 6 had been classified as such prior to the land surveys. • 10% of surveyed HH2 possess insufficient agricultural land for normal subsistence; this contrasts with earlier estimates based on declarative data of 67% of all HH as non-viable. • Developed and commenced implementation of 8 Site Specific Plans (SSP) for the villages of: 1. Begada 2. Bela 3. Danmadja 4. Dildo 1 Excludes Bero, for which precise land data not yet calculated 2 Excludes Bero since HH data has not yet been analyzed

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    5. Madjo 6. Mbanga 7. Mouarom 8. Ngalaba • Since Village Surveys show that most impacts of Project land acquisition occurred at the HH level, LUMAP included in the various 2009 SSP: o Plans for land return to the village o Supplemental community compensation activities to offset Project impact on communal resources o Resettlement options for HH identified as non-viable and affected by the Project 2. Livelihood restoration • Carried out livelihood restoration reinforcement training for 211 graduates trained in earlier years who showed promise but needed some extra help in restoring livelihood • Success rate of reinforcement training : 88% This number of individuals, whether or not truly Non-Viable, succeeded in restoring their livelihood or greatly improving it. 3. LUMAP 2009 Learnings • The primary impact of Project land acquisition has been on individuals and households, not on entire villages. • The impact of land acquisition on individuals and households has been much less than earlier Compensation Database reviews suggested. • Statistics derived from completed village surveys indicate that the number below the threshold for agricultural viability holds steady among villages and that land holdings among households are highly skewed. • The average of HH in the surveyed villages fell into the categories: o 10% under the agricultural viability threshold o 10% marginal farmers o 35% small farmers o 45% large family farms o 1% of vast land holders (often elderly people holding family land in trust) • Among compensated HH in the surveyed villages the average was: o 9% under the agricultural viability threshold o 8% marginal farmers

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    o 35% small farmers o 50% large family farms o 1% of vast land holders 4. Environmental Compliance • World Bank Group’s External Compliance Monitoring Group (ECMG) reassessed the LUMAP team’s tools, approach, and results in May and December 2009 and found that the plan and execution continued to be sound • LUMAP assisted the independent external environmental group ENVIRON in completing the field component of a Focused Environmental and Social Impact Assessment of the In Fill Drilling Program, which should appear in early 2010 The 2010 Go Forward Plan for LUMAP-EMP is to continue to identify people whom land acquisition puts at risk by using the LUMAP tools, to ensure Non-Viable people get resettlement benefits, and to monitor for 2 years all those at risk people receiving benefits to ensure they achieve livelihood restoration. LUMAP 2009 Overall Highlights In 2009, the LUMAP team carried out a number of activities directed at villages and at individuals directly affected by Project land acquisition. LUMAP activities continued to address the 9 Action Areas outlined in the Land Use Mitigation Action Plan: 1. Identification and Assessment of Impacts 2. Land Use Impact Reduction 3. Resettlement 4. Off-Farm Training 5. Improved Agriculture Training 6. Individual Compensation 7. Community Compensation 8. Consultation and Communication 9. Monitoring and Organization By the end of 2009 LUMAP had completed all these action areas, with the exception of one site specific plan that still required data analysis: Bero

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    For these Action Areas, in the course of 2009, the LUMAP team completed the following: 1. Identification and Assessment of Impacts • Finalized the land surveys of all village land (and land uses) and all households and household members in 10 villages that were on the LUMAP Watch List. • For 2009 this meant the remaining 6 Village Surveys and data analysis for 5 of them • Analysis shows that negative impact of land use is felt more keenly at the household (HH) level than by the entire village. No “village” can be said to be “at risk”. • In the 9 villages the percent of households at risk (NV or M) is 297/1589 or about 20% o The agriculturally Non-Viable HH are 153 or 10% of the 1589 HH ƒ 94 HH or 6% affected by Project land acquisition o The agriculturally Marginal HH are 144 or 9% ƒ 87 or 6% affected by Project land acquisition • Villages targeted for continued In Fill drilling are no different from other villages; the number of HH likely to be negatively impacted in future In Fill villages is minimal. 2. Land Use Impact Reduction • Restored and returned 144 ha of land • Developed and began implementation of Site Specific Plans in 8 villages. • Despite continuing land acquisition for In Fill drilling, LUMAP and the rest of EMP, with the invaluable assistance of EEPCI Construction and local businesses, kept the Project footprint the same size. Land return offset land acquisition. • Began trials of sustainable improvements in quality of land reclamation using green manure and compost generated from Project waste. • Carried out regional campaign to “green up” the countryside with extensive tree planting in the Oil Field Development Area. 3. Resettlement • As village surveys continued, some people were identified as being at risk although they had not been identified as vulnerable at the time their land was taken. They have been integrated into ongoing resettlement activities. • First attempts at 3rd Party Compensation to provide at risk HH with land showed that caution is required. Some of the individuals proposing themselves as donors were themselves at risk; in other cases, some fallow areas proposed by donors would bring them massive compensation but not necessarily be of agricultural benefit to the recipients. The program was halted until safeguards are developed.

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    • The potential of another land program which would avoid pitfalls of 3rd Party Compensation was evaluated. Improving state-owned lands along watercourses so that land-poor farmers can cultivate high-value rice using their traditional rice farming methods was approved by the government ministries involved, by EEPCI and by Africa Rice, the leading African research institution. • Actual resettlement: the “class of 20083”. The class of 2008 was the first to be confronted with the requirement to pass Basic Business Skills, Literacy and Numeracy training as a condition for Improved Agriculture or Off Farm training. A great number of them, fearing that they would be unable to meet the requirement, chose to acquire sufficient land through 3rd party land compensation. The complications of acquiring 3rd party land proved so time consuming that as a temporary solution they were offered both BBS and Improved Agriculture training in the upcoming rainy season, but without the equipment grant. Encouraged by their success in BBS and the farming techniques they were learning, many chose to switch from land to Improved Agriculture in order to gain the equipment they had desired but thought they could never attain. By 1Q 2010 when the equipment was being distributed, 135 had asked to change over to improved farming. Only 8 still wanted land; 17 had no other alternative than land as they had failed at their earlier livelihood restoration attempts and 2 were planning physical resettlement. 4. Off-Farm Training • Training in revenue-earning non-farm skills was not offered in the OFDA in 2009. (A LUMAP survey in 2007 showed the market was saturated). • Instead, already-trained individuals who had over time shown interest and ability in their craft received reinforcement training. Through reinforcement craftsmen worked at perfecting tasks that needed strengthening and acquired specialized skills (plus equipment) that would give them special niches in the market. The training organizations went to the villages to instruct, so that the craftsmen would be learning in normal conditions. 5. Improved Agriculture Training • In the same manner, reinforcement training was implemented for enterprising graduates of Improved Agriculture. A few were reinforced by agricultural equipment and equipment maintenance training but most received advanced training in their chosen off-season income-earning farm activity. 3 The eligible people choosing options are referred to as a “class” because so many of them choose training as an option that they all start their training at the beginning of the school year and they graduate together as well.

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    6. Individual Compensation • EMP continued to use the comprehensive 2008 compensation valuation study since regular inflation monitoring indicated no changes in value. 7. Community Compensation • A local NGO carried out 12 months of MARP (Rapid Rural Appraisal) consultation in the 15 OFDA villages where LUMAP would offset long-term land take by supplemental community compensation. • 8/15 villages have received their chosen Supplemental Community Compensation as of year-end 2009. Work is underway or will begin in 1Q 2010 in the remaining villages that have made their compensation selection. 8. Consultation and Communication • In each village, preceding the Village Survey, the survey team carried out public consultation on the purpose and the procedures for the survey. • MARP consultation helped villages understand their basic needs and ability to sustain a project; in addition the NGOs carrying out the MARP gained an overall view of the area similar to a “regional development plan”. • Discussion with the villages let to a streamlined “Quitus” process that returned reclaimed land to the farmers much faster. • Communication with implementing NGOs allowed EMP and the NGOs to derive important lessons learned on project implementation. 9. Monitoring and Organization • EMP-Information System (EMP-IS) database was improved to the level that it integrates all the Village Survey and HH data, responds to queries, and has been made available on the internal web.

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    2009 LUMAP ACTI VI TI ES Any discussion of LUMAP findings must be prefaced by an explanation of the Village Survey method and its capabilities. 1. Village Survey Capabilities By the end of 2009, LUMAP surveys had found everyone in surveyed villages that was non- viable, both those affected by Project land acquisition and those who remained untouched. The Village Survey tool has a number of advantages. 1.1. Brief Summary of Village Surveys I n 2007, the LUMAP team developed and put in place a data collection methodology and means of analysis permitting the quantitative identification of At-Risk households. The EMP- I nformation System database developed by the LUMAP team utilizes the Microsoft Sequel Server relational database and is able to import Geographical I nformation System (GI S) data from the sophisticated Global Positioning System (GPS) handheld devices used to measure a farmer’s fields and the detailed Socioeconomic Survey instrument. These devices are also handheld computers utilizing the ArcGI S software to synthesize. By the end of 2009 LUMAP had completely surveyed all the HH and individuals in the 10 villages that had been judged to be at risk in 2006 and had also linked all the land within the surveyed villages’ boundaries to the land users. How each piece of land was currently being used was also surveyed. The Village Surveys yield a clear view at a given moment in time of: • Village population size, age, sex and employment • Number of HH and their composition • Land holdings of HH and HHM • Land use map of village lands Many other specific questions concerning Project impacts on the HH were also included in the survey.

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    1.2. Detecting Non-Viable HH by Village Surveys In its Chad Resettlement and Compensation Plan (CRCP, vol. 3 of EEPCI’s Environmental Management Plan) agricultural “Non-Viability” is defined as “access to less than 2/3 corde4 of land (both cultivated and fallow), for each person [declared] as a member of [the] household.” This figure was derived from the Project’s baseline study of 468 HH plus CIRAD (now ITRAD) research institute studies on land use and fallow. The 2006 World Bank study of poverty in Chad derived similar numbers for all of southern Chad. The Project refers to the calculation of Non-Viability as “resettlement factor” or the point at which a HH falls below 2/3 corde per person in the HH. Non Viability = Land Holdings = Resettlement Factor # HHM Using Village Survey data on precisely measured fields and fallow plus the true number of HH members reveals which HH do not have enough land to be agriculturally viable. Of course this viability/Non-Viability refers only to the time at which the survey was done; what if measles suddenly killed off most of the children in the HH, or the HH head’s sister leaves her husband and moves in with her 6 children. Such events occur and rapidly change the HH’s size and its demands on land holdings. For this reason the system for tracking HH viability put in place by LUMAP requires that the survey be updated each time the Project compensates the HH for additional land. Once a Village Survey has been completed, it is easy to calculate the current resettlement factor for each HH. Looking through the list of resettlement factors of HH that have been affected by Project land acquisition shows precisely the HH that are non-viable and eligible for Project assistance. Additional data indicates whether or not the non-viable HH has been offered a choice of resettlement options and how well those options have done at restoring livelihood. At the end of 2009, how many Project-affected HH in the 9 villages remain Non- Viable or Marginal and vulnerable and without a resettlement option? (26) The Village Surveys show the extent of HH Non-Viability in a village and the order of magnitude of the poor livelihood problem. Looking across the board at the HH that fall into the categories “Non-Viable” and “Marginal” in the surveyed villages, it becomes evident that certain types of HH tend to fall into these two categories. Thus the village surveys orient 4 Corde = traditional land measure equaling about ½ hectare (5040 m 2)

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    LUMAP to seek out these types of HH when any future land acquisition is proposed, so that Project-caused negative impact can be immediately addressed. 1.3. Assessing Village Viability by Village Surveys The initial assumption leading to LUMAP was that Project land acquisition and the slow rate of land reclamation and return had put all the inhabitants of highly affected villages at economic risk. The task was, via Site Specific Plans (SSP), to identify and implement actions that would restore the village to viability. Defining these actions would be accomplished by consultation, analysis of existing data, and analysis of a Village Land Survey pilot that would be tried in Dokaidilti. The Dokaidilti survey demonstrated in a measurable way that the degree of negative impact in the heavily impacted village was much smaller than prior analysis of declarative data had indicated. Because it was able to pinpoint at risk households the Village Survey methodology became the basis for future SSP, although land reclamation and return remained a very high priority in reinstating villages. Village land surveys have the advantage of indicating the current division of village land between fields, fallow, and bush. It is easy to calculate what percent of the village’s land (fallow and bush) remains for potential future exploitation and the population density per km2. Using a formula including population size, length of cultivation cycles, etc. gives an approximation of the carrying capacity of the land (how many people can successfully live on a given area using their current farming techniques). A permutation of the formula gives the number of years an average farmer in the village can leave a piece of land in fallow to regain its fertility. Will it be long enough for fertility to be restored in the 9 villages? (Yes). The farming techniques in the OFDA are known (years in crops, desired years in fallow); comparing these needs to the potential length of fallow indicates to what degree the village is agriculturally viable. The same is true for the potential number of people that could be carried by the land. Is the number greater or smaller than the current population? (Greater). Another way of looking at Village Viability was initially employed by LUMAP using the kind of data that was available in 2007. When these results are compared to the precise population/agronomic results of the Land Surveys it is clear that the earlier criteria were more an evaluation of Project performance in land reclamation than a measure of direct Project impact on the community land pool. This earlier calculation concerned:

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    Amount of village land taken but not returned Total village land area According to these criteria the amount of village land taken falls into two categories: I. Land taken with the intention of using it for the lifetime of the project (e.g. camps, roads) II. Land taken temporarily (underground flow lines, ½ of each well pad) but not yet returned Category 1 should have been the measure of Project impact (how much of its land has a village lost for the estimated 23-30 year lifetime of the project?). But poor Project performance in land reclamation and return resulted in a good portion of the acquired temporary land remaining un-reclaimed and effectively out of cultivation for up to 5 years. For a correct view of Project impact the un-reclaimed temporary land is included as a sub-set of permanent land. As part of LUMAP, this earlier lag in temporary land return has been offset by LUMAP’s Supplemental Community Compensation. Using these initial 2007 categories of land take, out of 24 villages 5, then 7 were eventually classified as Highly Impacted and 3 as “Approaching High”. Since then the Project has developed and is implementing Site Specific Plans intended to reduce the backlog of unclaimed land for both categories I5 and II types of land. This not only fulfills Project obligations but helps individuals and families whose land should already have been returned to them and the community. But the Village Survey method shows that it is not the village as a whole that needs to benefit from the return. (Villages are not at risk). 1.4. Judging the Power of Traditional Ways to Adjust Population to Land Earlier published ethnographic studies of the area have uncovered the ways in which the population of the area has traditionally dealt with land pressure and related social tensions. Are the coping mechanisms studied in the 1970s still in force today, and are they powerful enough to deal with the additional pressure on land created by Project land take? The reliability of population data available to answer this question varies, but enough information is available from pre-Project times (the National Census in 1993, for example) to judge whether traditional coping mechanisms were still at work just before the Project began. (They were). 5 Not all permanent construction was in fact carried out. I f well performance indicated that a certain area would not produce much oil, nearby well pads, access roads, etc. could be reclaimed and returned.

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    Population data collected by village and canton chiefs for their administrative tasks are also available for the period during which the Project has been implemented: 2000-2009. Starting in September of 2007, precise population data from the Village Surveys has also become available and can be analyzed. Have the same traditional coping mechanisms been expressed during the Project period or have they been overwhelmed? (They are still at work). If traditional methods are still active, then their power is conjoined with specific Project interventions – resettlement options and land return in particular – to restore, maintain or augment HH viability. This depends in part on how well HH receiving resettlement options have used the option but they always have recourse to the traditional ways of restoring their livelihood or do better than before. (On the whole, HH do pretty well with resettlement options). 2. Village Survey Results 2.1. Overall Picture of Surveyed Villages Critical facts about each village are its population (how many people are in the specific Project-affected area?); number of households (number of social units being affected?); population density (what is the intensity of social interaction/tension?); and average length of possible fallow (What is the pressure on productive resources, demand for farmland and sustainability of current farming system?). What differences are there within villages in terms of status, family size, and households (do all people share equally in Project impact? Are there vulnerable people?)? Given that traditional means of coping with social pressures and land shortage are still in play, do the people that resort to these traditional measures put themselves temporarily at an economic disadvantage that Project actions could aggravate? Answering questions with hard data: Population Figures and Productive Resources Length of Village Population # HH People/Ha Fallow Danmadja 570 101 1.19 6.02 Dokaidilti 533 85 0.78 10.32 Dildo 1 326 268 0.70 12.93 Ngalaba 1 318 249 0.62 15.12 Mbanga 1 497 269 0.49 26.46 Madjo Bero 839 131 0.39 26.67 Begada 1 287 259 0.39 26.70

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    Bela 830 144 0.38 27.53 Mouarom 444 84 0.33 32.23 Bero 3 897 311 na na Initial comparison of the village populations and the number of HHs gives the impression that villages are very different from one another. But looking at population density and length of fallow reduces the impression of differences by comparing villages’ use of village resources. To answer the questions: • People in the 9 villages 8 634 • Number of social units in area 1 589 • Intensity of social interaction Low 5 villages (density 0.33-0.49) Medium 3 villages (density 0.62-0.78) High 1 village (density 1.19) • Possible length of fallow More than adequate 5 @ 20+ years Adequate 3 @ 10+ years Barely sufficient 1 @ 6 years The village with “high density” and “barely sufficient” fallow will be examined in section 5 below. The number of HH in the Project area is related to HH size: big HH mean a smaller number of HH in the village but more intense interaction within the HH. HH size is determined in part by biological factors (births, deaths) but in good part by social factors. The chief factors which operate in the OFDA are: • Birth of children which drives establishment of an economically independent HH • Divorce or widowhood (plus the intermediate stage in this culture of “separated”) • Social obligations to care of people other than the nuclear family • Old age But another important factor driving HH size in many societies is: • Access to productive resources The last point is important to understand within the Project context and need for land. Are there social distinctions that limit access to productive resources? Has the Project’s land acquisition reduced HHs productive resources and increased HH size and HHM interaction, which could lead to more conflict? As said, most of the factors listed above appear to be driven by biological events but they are also expressions of the culture’s way of viewing and treating the different stages of life. For

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    example, in some societies a couple would become an economically independent entity directly upon marriage; here males become residentially independent in their bachelor teens and begin the steps towards economic independence. Here the act of marriage may or may not lead the couple to establish a separate HH but then, with children coming along, the couple becomes economically independent. At a later stage the wife may become economically independent, either staying in the same homestead or moving elsewhere. The nature of the domestic cycle in the OFDA plays an important role governing access to resources i.e. the need for resources finds a general cultural, not simply biological, expression. But this expression does not operate at the village level, as the table below shows. There is no relation – at the village level – between access to productive resources and HH size. Project pressure on assets does not affect “villages” and create more intense, and possibly negative, social action among the village households. Villages as a social unit are not at risk. Nor does Project pressure drive HH to increase in size and interaction because more people must exploit the unvarying HH productive assets. Village People/Ha Avg. HH Size Danmadja 1.19 5.6 Dokaidilti 0.78 6.3 Dildo 0.70 4.9 Ngalaba 0.62 5.3 Mbanga 0.49 5.6 Madjo Bero 0.39 6.4 Begada 0.39 5.0 Bela 0.38 5.8 Mouarom 0.33 5.3 2.2. Traditional Coping Mechanisms in Surveyed Villages Are the HH in villages that have resorted to traditional coping mechanisms at higher risk because they have abandoned the assets they had been exploiting? (No). 2.2.1. Village Splits The most common traditional coping mechanism at the village level is for the village to split. Initially when a HH does not get along with its neighbors it seeks separation and moves to the far end of the village or out of it. Or when the village is large enough for people to start building factions and gain supporters, an individual with aspirations to influence will hive off and settle on a distant part of village land or in another area entirely, where the HH is more autonomous. Sometimes partisan HH that have followed their leader find that the seeming

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    opponent’s pasture was indeed greener, or the querulous HH fails to increase. In these cases hamlets die out. But often the hamlet becomes the nucleus of a new village exploiting the area on which it has settled. Eventually the new village will ask for administrative recognition of its separate status as a new village. Of the 10 surveyed villages 5 have completed a split during the time the Project has been running and 4 are in the process of splitting. They are not alone among the OFDA villages. A comparison of HH numbers, population size and access to productive resources shows that split villages are identical twins. There are no characteristics that would put one part of the village at a disadvantage vis à vis Project impact. In fact, in terms of differences in land distribution among HH, all villages demonstrate similar proportions of wealthy, comfortable, Marginal and vulnerable HH.

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    Comparison of Population and Land Availability Between Former Quartiers of Split Villages Be ga da D a n m a dj a D a n m a dj a N gala ba N ga la ba Be ga da 2 Be la 1 Be la 2 D ildo Ba ya n de D ok aidilt i Ba ne la ou W olo 1 1 2 1 2 population quartier 669 624 474 374 638 716 398 138 369 210 512 694 118 % population quartier/total 52% 49% 45% 45% 47% 53% 75% 26% 65% 37% 39% 53% 9% population population density 0.22 0.23 0.25 0.27 0.49 0.39 na na 1.75 2.07 0.48 0.37 0.55 people/corde population density 4.49 3.87 4.06 3.65 2.03 2.55 na na 0.01 0.00 2.09 2.71 1.83 cordes/person # HH in quartier 151 109 79 65 127 148 59 26 61 41 102 129 19 % Quartier HH/ All HH 58% 42% 55% 45% 47% 54% 69% 31% 60% 40% 41% 52% 8% Avg. corde/HH in 20 22 4 7 10 12 11 12 11 11 10 15 11 quartier now Avg corde/capita in quartier 6 6 24 21 2 3 2 3 2 2 2 3 2 now

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    Comparison of Land Distribution Between Former Quartiers of Split Villages Per Capita Land Holding Begada 1 Begada 2 Bela 1 Bela 2 Dildo Bayande Number Zero land 0 1 6 6 3 2 Number Non Viable (<2/3 cde) 4 10 37 37 18 14 Number Marginal (2/3 - <1 cde) 4 7 0 0 21 18 Number Comfortable (1 – 2/5 cde) 35 22 5 7 49 62 Number Wealthy (2.5-10 cde) 95 53 4 3 31 45 Number Wealthy (10-20 cde) 8 9 19 13 5 5 Number Wealthy (20-30 cde) 3 4 46 35 1 1 Number Wealthy (30-40 cde) 1 1 5 0 0 0 Number Wealthy (40 + cde) 1 1 0 0 0 0

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    EMP 2008 Annual Individual Report on Vulnerability Page i 2.2.2. Non-Agricultural Productive Resources Fishing is another coping mechanism available to some villages only. Not all families in fishing villages engage in the practice and some use their catch just to feed the family whereas others go fishing for the extra income it brings. Presented with a substantial fish sauce the family needs less staple cereal to be satisfied and well nourished so a fisherman’s family does not need to grow as much. The extra income from fishing is available throughout the months that cereal is cheaply available in the market and the adults can round out the family’s staple needs; most fishermen earn between 20-30000 f CFA per month during the 6 months when fishing is good. Many families in fishing villages do not need as much land as inland farmers to maintain an adequate standard of living. Fishing diminishes the need for land as a productive resource. Fishing Villages People/Ha Years Fallow Danmadja 1.19 6 Dildo 0.71 12 Dokaidilti 0.78 10 Madjo 0.39 26 Inland Villages People/Ha Years Fallow Begada 0.36 26 Mbanga 0.49 26 Mouarom 0.33 32 Bela 0.38 27 Ngalaba 0.63 15 Although Madjo Bero and Bela are fishing villages they are anomalies. Madjo Bero has more than ample land because during the Project period it has undergone a definitive split, with part of the village moving across the river into another Canton/Sub-Prefecture. They now farm in Madjo Doba. Bela’s situation is ambiguous; while it has plenty of land there are some inhabitants who are serious fishermen. But accessing the river from Bela is more difficult and recreational fishing for the family table is less common. 2.3. Gender in Surveyed Villages Cultural attributes such as economically independent women with their own productive resources can be observed at the village level. In this culture, women are allowed economic independence and inherit family land along with their brothers, as the following data show.

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    EMP 2008 Annual Individual Report on Vulnerability Page ii Range of Land Holdings by Gender (Begada, Danmadja, Dildo, Dokaidilti, Mouarom, Ngalaba) 60 50 40 Nbr HHH 30 20 10 0 0 - 0.9 10 - 10.9 20 - 20.9 30 - 30.9 40 - 40.9 50 - 50.9 67 - 67.9 100 - 100.9 Cordes per HH Nbr HHH Men Nbr HHH Women The village surveys show the extent to which women become the heads of their own HH and no longer have the protection of a husband and the husband’s household resources. Distribution of Land Holdings by Gender of HHH 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% a a ja om la ilti o ab d ld ad Be id ga ar Di al ka nm Be ou Ng Do Da M percent male HHH % land "owned" by men percent female HHH % land "owned" by women

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    EMP 2008 Annual Individual Report on Vulnerability Page iii As women can be self-reliant because of their right to productive assets, so Project actions which act on productive resources can also directly affect women. # # % FHHH / Village #HH MHHH FHHH Males Females All HHH Dokaidilti 85 243 290 75 10 12% Bela 144 428 402 126 18 13% Danmadja 101 284 286 87 14 14% Mouarom 84 213 231 72 12 14% Madjo Bero 131 412 427 112 19 15% Dildo 268 649 677 213 55 21% Mbanga 269 717 780 205 64 24% Bero 311 1924 1945 527 84 27% Begada 259 608 679 187 72 28% Ngalaba 249 664 654 173 76 31% When 20-30% of village households are headed by women, a significant part of the women in the village are open to un-mediated direct impacts on their assets. 2.4. Age Distribution in Surveyed Villages In all the surveyed villages the average age of the population is young. This means that there are many non-productive inhabitants in the village who must be sustained by the adults in charge. Project actions affecting a few adults can result in affecting indirectly quite a few people. Avg Age All Avg Age Avg Age Village Males Females Population Begada 18.5 20.5 19.5 Bela 18.3 20.1 19.2 Bero 17.3 18.8 18.1 Danmadja 19.8 19.5 19.6 Dildo 19.8 22.2 21.1 Dokaidilti 19.8 21.0 20.4 Madjo Bero 16.6 19.4 18.0 Mbanga 17.4 21.2 19.3 Mouarom 19.8 20.4 20.1 Ngalaba 18.9 22.2 20.5 Not only is the average age young; the preponderance of household heads (HHH) in all surveyed villages is also young and this group of HHH will bear the brunt of Project impacts on productive resources simply because demographically they are the most

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    EMP 2008 Annual Individual Report on Vulnerability Page iv numerous group in the population. Demographics also play a role since more women live to an older age and, as has been shown, they are often independent and have their own assets.

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    EMP 2008 Annual Individual Report on Vulnerability Page i Peak Ages for Vulnerability Distribution of Eligible HH by the Age of the Household Head 9 N u m b e r E lig ib le H o u s e h o ld 8 7 6 5 Nbr Eligible HH 4 3 2 1 0 16 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 A ge

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    EMP 2008 Annual Individual Report on Vulnerability Page i 2.5. Gender As a Mediating Factor When the Project definition of agricultural Non-Viability resulting from Project land take -- a definition used to decide if a person is eligible to choose a resettlement option -- is applied, the cultural influence on how households are formed leads to a seeming large number of Non-Viable young male HHH. Gender thus appears to be a mediating factor, making young men particularly vulnerable. Comparison of Vulnerability by Age in Dildo, Dokaidilti, Ngalaba and Danmadja Villages Age % Male HHH Non-Viable % Female HHH Non-Viable >20 0 0 20-29 19 0 30-39 8 3 40-49 5 3 50-59 3 4 60-69 1 5 70-79 0 0 80+ 0 0 But looking at available land the village level shows that the villages have plenty of land. Social analysis also shows that many seemingly vulnerable young HH only appear that way. A young male HHH looks as though he has only a few fields for his HH members because his HH has only started farming and has not yet reached the point where a piece of land has been farmed long enough for it to need to be fallowed. As soon as the young male farmer needs more land because of declining fertility he will ask the old man for access to some of the family trust fund. Only the unlucky HH that are just starting out in life without any trust fund of land to draw on are, in fact, non-viable and will probably remain so unless traditional mechanisms are called into play. The unlucky male HHH without a family trust tend to be children of men who have moved in to the village from the outside. If they have moved to join their mother then they have access to her family’s land, but their residual rights do not have precedence over a large number of maternal cousins. A man’s decision to move to another village (which occurs

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    EMP 2008 Annual Individual Report on Vulnerability Page ii for many reasons) has for his heirs the consequences of limiting the family trust to the amount of land he is able to convince his host, or village chief, and land priest to give him. For the majority of males, living in their natal, and their father’s natal, and their father’s natal village there is always the family trust to draw on. The balance between HH size and the amount of land belonging to the HH demonstrates this slow transfer from those in the HH according to their ability to give unto those relatives according to their increasing need for land. (See following graph). The few HH without any inheritance to call on (left hand side of graph) cannot attain this balance in their current residence. Distribution of Land Among HH by Size (Begada, Danmadja, Mouarom) 9.00 8.00 7.00 6.00 HH Size 5.00 4.00 3.00 2.00 1.00 0.00 4 to 6 6 to 8 0 <2/3 <1 <2.5 2.5-4 8 to 10 10 to 12 12 to 14 14 to 16 16 to 18 18 to 20 20 to 25 25 to 30 30-40 40 + Size of Landholding

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    EMP 2008 Annual Individual Report on Vulnerability Page iii Number of HH at Different Ages with the Amount of their Landholdings Landholdings of HH by Ethnic Age Categories 250 200 Zero Number of HH 150 < 2/3 <1 100 < 2.5 2.5 + 50 0 <20 20-24 25-29 30-44 45-69 70+ Age Categories By comparing HH at the village level this age phenomenon becomes evident. All village HH are not in trouble, lacking productive assets, only a few. (10%) Age and gender are mediating factors in the development of vulnerable female-headed HH as well. Young women separate from their natal families when they marry/have children. At this point in the new HH’s domestic cycle, the husband should, according to the cultural norms, be primarily responsible for his wife and children (the wife being tied down at home by child care). The age distribution of female HHH shows this norm is largely respected. With time the marriage bond may change and the wife undertake more productive (rather than reproductive) activities to complement her husband’s support to herself and her children. It is the degree of “support” that is/should be given that is the most contentious issue in marriage, leading to numerous traditional court cases, separations and divorces. The increasing independence of women as they grow older is clear in the village surveys. The fact that women become independent as they age means they must have productive resources of their own to support the independent family. As a consequence, any factor that affects these resources affects these women directly. Older female HHH with limited

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    EMP 2008 Annual Individual Report on Vulnerability Page iv productive resources tend to be non-viable or Marginal farmers. Most often they are women who have married outside their natal village where they would still be able to access family land. Comparison of Vulnerability by Age in Dildo, Dokaidilti, Ngalaba and Danmadja Villages Age % Male HHH Non-Viable % Female HHH Non-Viable >20 0 0 20-29 19 0 30-39 8 3 40-49 5 3 50-59 3 4 60-69 1 5 70-79 0 0 80+ 0 0 It is the few HH characterized by these vulnerability factors tied to age and gender that are more open to feeling Project impact. Later examination of individual HHs will reveal the nature of HH vulnerability. 2.6. Status Differences in Surveyed Villages The answer to the question whether there are differences in social status that prevent equal access to productive resources is “NO”, as these tables and charts indicate. It is the other way around; social status is measured by access to productive assets, with its links to age and gender and family trust funds. But, and herein lies the rub, it depends on how you use those assets that gains you social status. 30 cordes in the family trust fund gets you nothing, but put some of that into farmland and be generous with the harvest and your status rises. Status depends on your interest and ability in farming and on your relationship with other villagers. Comparing HH at the village level shows that there are indeed differences within each village in terms of land holdings. There are a few landholders with such exceedingly large tracts of land that all the HHM could never succeed in cultivating even half; it is these land holders who have lands lying fallow for 30-40 years or more. They are the senior members of the

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    EMP 2008 Annual Individual Report on Vulnerability Page v family holding the family land in trust but with too few successors that have demanded land and whittled down the land trust (low number of births, lack of interest in farming, an heir already farming to his maximum). Upon his or her death the heirs will receive packages of land that are likewise large and may lie unfarmed. (See graph “Number of HH at Different Ages with the Amount of their Landholdings” above). And yes, elderly women can have extensive holdings: Age and Gender of Land Holders over 30 Cordes Per Capita Village Male Land Holder Ages Female Land Holder Age Begada 2 48; 61 3 73; 71; 75 Bela 2 50; 63 0 Danmadja 0 0 Dildo 0 1 76 Dokaidilti 0 0 Mbanga 0 1 65 Mouarom 0 0 Ngalaba 0 1 59 Per capita land holdings as large as these are the exception but in the surveyed villages most HH are land wealthy, with large farms of more than 2.5 cordes per HHM, 10 to 20 cordes per HHM. Another large group is comfortable land holders, with 1 to 2.5 cordes per HHM. Fewer HH are Marginal or Non-Viable. The presence of HH with no land at all can be explained either by other sources of HH income, support of children, or because the person has borrowed/rented land from someone else. 2.7. Land Holding Differences Within Village It is above all this skewed distribution of land, skewed in the same way in all the surveyed villages, which leads to the conclusion that villages as a whole are not at risk. Instead, specific HH lack access to productive land (see tables below and Annex B for more detailed tables). What can be the causes of this disparity in access to resources? We have seen that age gender, and village of origin play a role but that social status differences do not. What about Project impacts?

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    EMP 2008 Annual Individual Report on Vulnerability Page vi Size of Land Holdings by Age (Dildo, Dokaidilti, Ngalaba) 120 <20 Nu m b er o f HH 100 80 20-23 60 24-30 40 31-44 20 0 45-59 zero land <2/3 <1 <2.5 2.5+ 60-69 Amount of Land 70+ 2.7.1. Project Compensation and Land Acquisition as a Cause of Non-Viability or Marginality? Differences in land holding status clearly exist in the surveyed villages and a similar distribution of land is found in each of them. But perhaps the Project’s land acquisition has affected all HH equally, whatever their prior status, and created similar numbers of Non- Viable and Marginal HH throughout the population. (It has not).

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    EMP 2008 Annual Individual Report on Vulnerability Page i Per Capita Land Distribution (Cordes per Capita) % ALL HH Begada Bela Danmadja Dildo Dokaidilti Madjo Bero Mbanga Mouarom Ngalaba Average zero 0% 0% 1% 2% 0% 2% 0% 3% 1% 1% <2/3 6% 8% 15% 11% 12% 12% 6% 5% 7% 9% <1 5% 5% 21% 15% 15% 9% 6% 3% 8% 10% <2.5 22% 22% 39% 40% 42% 43% 30% 36% 41% 35% 2.5 + 68% 65% 25% 32% 31% 34% 57% 53% 42% 45% Distribution of Individual HH per Village by Per Capita Land Holdings 0 to 2/3 to< 1 to Village All/Compensated Zero <2/3 1 <2.5 2.5 + Begada All 2 13 12 57 176 Compensated 2 9 10 40 129 Bela All 0 12 7 32 93 Compensated 0 5 1 16 60 Danmadja All 1 15 21 40 25 Compensated 1 12 17 37 21 Dildo All 5 31 40 111 88 Compensated 1 10 16 45 31 Dokaidilti All 0 10 13 36 26 Compensated 0 9 12 31 21 Madjo Bero All 3 16 12 57 45 Compensated 2 10 51 39 Mbanga All 0 17 16 82 153 Compensated 0 10 13 63 125 Mouarom All 3 4 3 31 46 Compensated 2 4 3 26 39 Ngalaba All 2 19 20 103 106 Compensated 0 17 15 85 92 Total All 16 137 144 549 758 Compensated 8 86 87 394 557

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    EMP 2008 Annual Individual Report on Vulnerability Page ii Village Impact Classification According to Number of HH (not individuals) Land Holding Status All HH & % Non-Viable % Non-Viable HH / Village Compensated Compensated HH/ All HH HH Compensated HH Begada All 5.8% Compensated 4.2% 5.8% Bela All 8.3% Compensated 3.5% 6.1% Danmadja All 15.7% Compensated 12.7% 14.8% Dildo All 13.1% Compensated 4.0% 10.7% Dokaidilti All 11.8% Compensated 10.6% 12.3% Madjo Bero All 14.3% Compensated 9.0% 10.8% Mbanga All 6.3% Compensated 3.7% 4.7% Mouarom All 8.0% Compensated 6.9% 8.1% Ngalaba All 8.4% Compensated 8.1% 8.1% Total All 9.5% Compensated 8.2% 8.2% High 15.10% Approach High 10.10% Moderate 5.10% Low 0.00%

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    EMP 2008 Annual Individual Report on Vulnerability Page i Examining this question is not straightforward because the declarative data given at land acquisition and recorded as “baseline” is unreliable. Using this declarative data to determine a HH’s pre-Project land holding status would yield little useful information on this earlier status. Instead the GIS-measured amount of land taken from the HH, from Project inception to date, has been added to the GIS-measured land belonging to the HH at the time of the village survey. Some HH may have gained or lost land during this time through the operation of cultural factors, so the initial situation was somewhat different, but the sum of the two land measurements is a certainty. For calculation purposes the number of HHM counted during the village survey has been used as the number of HHM prior to Project land acquisition. This is an assumption since HH grow and shrink as the domestic cycle turns, but it gives an objectively measured number and, for HH compensated late in the Project it is close to today’s situation. Pre-Project land holding status for a HH has been calculated thus: ∑ Land measured by Project for compensation + Land Holdings measured by Village Survey Number of HHM counted by Village Survey This gives an approximate cordes per capita (or “resettlement factor”) measure just before the HH’s first land compensation. The following tables compare the number of Non-Viable and Marginal HH, plus other HHs’ land holding status “pre-Project” and today. # All Mbanga HH at Resettlement Factor Male HHH Female HHH Total # current HH HH viability factor 203 63 269 before now before now Zero 0 0 0 0 0 <2/3 17 5 8 8 9 <1 16 8 12 5 4 <2.5 82 59 65 15 17 2.5 + 154 134 121 35 33

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    EMP 2008 Annual Individual Report on Vulnerability Page ii # All Madjo HH at Resettlement Factor Total # current HH Male HHH Female HHH HH viability factor 133 before now before now zero 3 1 3 0 0 <2/3 16 11 13 3 3 <1 12 11 11 0 1 <2.5 57 46 52 6 5 2.5 + 45 50 41 5 4 # All Bela HH at Vulnerability Factor Per Capita Male HHH Female HHH HH viability factor before now before now zero 0 0 0 0 <2/3 6 9 3 3 <1 8 6 1 1 <2.5 27 28 3 4 2.5 + 86 84 10 9 # All Mouarom HH at Factor Per Capita Total # current HH Male HHH Female HHH HH viability factor 87 before now before now zero 3 0 2 1 1 <2/3 4 5 4 0 0 <1 3 0 2 0 1 <2.5 31 22 25 7 6 2.5 + 46 48 42 4 4 # All Danmadja HH at Factor Per Capita Total # current HH Male HHH Female HHH HH viability factor 102 before now before now zero 1 0 0 0 1 <2/3 15 10 13 2 2 <1 21 7 16 2 5 <2.5 40 41 35 8 5 2.5 + 25 30 23 2 2 # All Begada HH at Factor Per Capita Total # current HH Male HHH Female HHH HH viability factor 259 before now before now zero 0 0 0 0 0 <2/3 17 5 8 8 9 <1 16 8 12 5 4 <2.5 82 59 65 15 17 2.5 + 154 134 121 35 33

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    EMP 2008 Annual Individual Report on Vulnerability Page iii # All Dildo HH at Factor Per Capita Total # current HH Male HHH Female HHH HH viability factor 272 before now before now zero 5 4 4 0 1 <2/3 31 16 20 11 11 <1 40 27 32 8 7 <2.5 111 101 95 16 16 2.5 + 88 69 65 23 23 # All Dokaidilti HH at Factor Per Capita HH viability factor Total # current HH Male HHH Female HHH 85 before now before now zero 0 0 0 0 0 <2/3 10 4 7 3 3 <1 13 6 11 2 2 <2.5 36 41 31 2 2 2.5 + 26 24 23 3 3 # All Ngalaba HH at Factor Per Capita Total # current HH Male HHH Female HHH HH viability factor 250 before now before now zero 2 0 0 2 2 <2/3 19 8 12 6 7 <1 20 13 14 4 6 <2.5 103 63 73 21 30 2.5 + 106 90 74 36 32 Project land acquisition could not be the only source of Non-Viability or Marginal status because HH already found themselves in these categories before Project activities began. In spite of Project land acquisition the great majority of HH today are either large or comfortably well off land holders. 2.7.2. Project Compensation and Land Acquisition and the Creation of Resource Haves and Have-Nots? Comparison of those who have received Project compensation because of Project land acquisition shows not only that the percentage of the population that has received compensation is similar in most of the surveyed villages but also that a majority of HH in a village have received compensation. The number of HH in one village having received compensation is essentially the same as in the others.

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    EMP 2008 Annual Individual Report on Vulnerability Page iv % of HH receiving % HH unaffected directly Village compensation and no compensation Begada 73 27 Bela 53 47 Danmadja 87 14 Dildo 37 63 Dokaidilti 86 14 Madjo Bero 83 17 Mbanga 78 22 Mouarom 85 15 Ngalaba 84 16 In 7 of the villages – not Bela and Dildo – almost ¾ of the households have received compensation at one time or another, some HH several times, during the Project. In 4 6 surveyed villages more than 80% have received compensation . By contrast, Bela and Dildo, whose village lands lie only partially over the oil-bearing fields, have received less compensation since there was little connection with Project land needs. Do these villages less affected by the project have fewer at risk people, demonstrating that where the Project impact is less the HH situation is better? The answer is a clear “NO”; compensation does not automatically lead to an increase in Non-Viability. Many other factors must enter as well or else how to explain how the least compensated village has more Non-Viable and Marginal HH than the average village in the survey? Non-Viable and Marginal HH in Less Compensated Villages Village % Non-Viable HH % Marginal HH Bela 8 5 Dildo 13 15 Average Surveyed Village 10 10 Other cultural factors make HH Non-Viable or Marginal beside Project land acquisition. It is not surprising, therefore, that the society has developed traditional coping mechanisms. 6 The economic impact on the ¾ of the population will be discussed in the section on how much land the Project has acquired from single HH, section 4.1.

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    EMP 2008 Annual Individual Report on Vulnerability Page v 3. Outside Pressure on Local Productive Resources It is a widespread belief elsewhere in Chad, and among lenders and NGOs, that the presence of the Project in the OFDA attracted a population influx of job seekers, creating extensive pressure on local resources. Village surveys demonstrating the current demand on natural resources; when conjoined with past population information, illustrate the extent to which Project job-seekers have had an impact on area resources. (Very little). 3.1. Outside Project Workers Who Settle in the Villages Most of the workers on the Project who came from other parts of Chad lived, not in the villages or even in the OFDA, but in the two major urban centers of Doba and Bebedjia. 89% of national workers lived in these two towns7 and took Project-provided transport to and from work. Some of the local workers also shifted to the towns because transportation was easier. The OFDA village populations were increased by only 0.3% of outsiders; most of this increase occurred in the canton centers: • Kome Ndolebe added 2.2% of its pre-Project size • Bero added 1.3% of Bero’s earlier population • Miandoum added 0.5% increase of the village’s inhabitants Any influx that occurred did not, therefore, impact most villages’ resources. 3.2. Spontaneous Settlements/ Shantytowns Outsiders did come in numbers and settle in one part of the OFDA, at Kome Atan. Atan became a spontaneous settlement and in its early days, before settlers had time to build solid buildings, a shantytown. But most of these people came with their families and with the intention of settling there for as long as they found commercial benefits to support themselves. Kome Atan lies opposite the Project construction camp and was originally established by oil exploration workers when they moved their families, along with the camp, from Sarh to Kome Base in 1994. They and their families rented farmland from Bela, the village where the camp was built, and Atan was initially referred to also as Bela 2. The settlement earned a new name as it attracted service providers from elsewhere to cater to the oil-workers needs. In 1998 the settlement contained 94 HH. At the height of Project construction it had grown by 91% as merchants and service workers moved in to take advantage of the business 7 I t must be remembered that the “total number of workers hired by the Project” differs from the number of workers engaged by the Project at any one time. Construction demands different skills at different times, so workers did not remain Project employees for the entire duration of construction.

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    EMP 2008 Annual Individual Report on Vulnerability Page vi opportunities. Very few of the newcomers were job seekers, as the Project’s policy of “No Hiring at the Gate” proved an effective deterrent. As with the original oil workers, Kome Atan inhabitants have a solid source of non-agricultural income (salaries, business revenues); but they also rent (and in some cases “buy8”) a small bit of farmland which other members of the family cultivate in the rainy season. The villages which have provided farmland are Bela, which has a population density of 0.38 people per hectare and enough land to keep a field in fallow for 27 years, and Mouarom, with a density of 0.33 people per hectare and 32 years of fallow. The population of Atan adds some pressure to the resources of villages that possess ample land (see Maps in Annex 3). 3.3. A Burgeoning Population and Internal Pressure Rather than outsiders or the Project putting considerable pressure on local productive resources, it has been the population increase of the local inhabitants that has had most impact. Villagers will often complain that there is no land left for fallow. But the surveys have shown that for most villages there is plenty of land for a long period of fallowing. What their complaints effectively reflect is the great population increase that has occurred in the recent past, which puts more pressure on fields near the village settlement. The true nature of the complaint would be: I have too few nearby fields to leave them in fallow for very long; because there are more people with such a high birthrate I am going to have to turn sooner to traditional coping mechanisms and get up and move someplace where there is unused farmland. In 1993 there was a national census throughout the country collecting village (and quartier) population figures. Village chiefs on a somewhat annual basis count their populations because they are answerable for their citizens’ poll tax payments. In late 2000 and early 2001 the main contractor for Project construction carried out a population count in nearby villages to estimate the labor pool and possibilities for housing workers; this count is probably more reliable that chiefly counts, which may or may not have been updated for that year. From these three sources plus the village surveys it is possible to observe the population growth over 20 years. Between 1993 and the beginning of the Project the local population underwent a major increase. 8 A voluntary transaction which has no official standing as the land has not been registered with the government.

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    EMP 2008 Annual Individual Report on Vulnerability Page vii

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    EMP 2008 Annual Individual Report on Vulnerability Page i Population Change from 1993 to 2008/9 Village Population Density People Per Hectare Change Between Years Year 1993 2000 2008/9 Delta 1993-2000 Delta 2000-2008/9 Begada 0.18 0.30 0.36 0.30 0.12 Bela 0.15 0.27 0.38 0.12 0.11 Danmadja 0.45 0.88 1.19 0.88 0.43 Dildo 0.37 na 0.71 na na Dokaidilti 0.24 na 0.78 na na Madjo Bero 0.16 0.53 0.39 0.37 - 0.14 Mbanga 0.18 0.44 0.49 0.26 0.05 Ngalaba 0.40 0.65 0.63 0.65 - 0.02 Negative rates of increase are a sign that part of the population has hived off and departed elsewhere.

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    EMP 2008 Annual Individual Report on Vulnerability Page i The difference in the rate of growth between1993-2000 is strikingly larger than the rate of growth once the Project began in 2000 and on up to 2008/9. If the population density in these villages about doubled before the project and grew only by a few points after Project inception, or decreased, the demographic impact of the Project was not as great as natural population increase. 4. Deriving Project Impact on Household Productive Resources from Village Surveys The Village Surveys present the situation of everyone within a village, not just those who have been compensated. Analysis of such inclusive data has provided understanding of the nature of villages, their size, their composition, and their productive resources. It also allows inter-HH comparison of all HH within a village and between villages. 4.1. Effect of Project Land Acquisition on Household Productive Resources Using the Compensation Database to compare the totals of the amount of land acquired from any single HH shows that only rarely has the Project acquired large parcels of land from a single HH. Most land acquisition involves long, thin corridors of a few meters width for flowlines, roads, power lines and so forth. The largest take would come when a well pad falls entirely within one HH’s fields, more when the HH has many fields overlying a rich oil-bearing spot where a number of well pads will be built.

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    EMP 2008 Annual Individual Report on Vulnerability Page ii Land Taken from At-Risk Households in Begada, Danmadja, Dildo, Dokaidilti, Mouarom, Ngalaba 35 30 Number of At Risk Household 25 20 15 10 5 0 0 - 0.9 1 - 1.9 2 - 2.9 3 - 3.9 4 - 4.9 5 - 5.9 6 - 6.9 7 - 7.9 8 - 8.9 9 - 9.9 10 - 10.9 11 - 11.9 12 - 12.9 13 - 13.9 19 - 19.9 22 - 22.9 Compensated Area Range (corde) # HH Eligible When the precisely measured totals of land taken from HH (graph above) is compared to the Village Survey graph of land distribution shown below, it becomes evident that the small total amount of land usually acquired from a HH is a minor part of most HHs’ land holdings. In other words, Project land acquisition has not had a major impact on large or medium size land holders. It is when small land holders (Non-Viable or Marginal) surrender a piece of land, even though a small piece, that the HH feels an impact. The degree of negative Project impact on productive resources is related to the proportion of HH in a village that is already at risk.

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    EMP 2008 Annual Individual Report on Vulnerability Page iii Distribution of Land in 9 Surveyed Villages 160 140 Begada 120 Bela Danmadja Number of HH 100 Dildo 80 Dokaidilti Madjo 60 Mbanga 40 Mouarom Ngalaba 20 0 0 <2/3 2/3 - < 1 1 - < 2.5 2.5 - 10-<20 20 - <30 30 - <40 40 - < 50 + <10 50 Num ber of Cordes From the data available in the Village Surveys we know what proportion of HH is at risk (both Non-Viable and Marginal) – 19% – and who they are. The specific HH that are Marginal and have little land and could be made Non-Viable by additional Project land needs are also identifiable. Finally, Non-Viable or Marginal HH that have never been affected by land acquisition are also identifiable; in case their situation changes and the Project compensates a piece of their land any change in status can be calculated. 4.2. Changes in HH Landholding Status Within each village land per capita tends to be distributed in clusters, each trending around a different size land holding. Zero land holdings are rare; less than 2/3 corde and Marginal (less than 1 corde) tend to run together, reflecting the easy slide from Marginal to Non- Viable. Indeed the 2/3 corde per HHM is an “artificial” divide, reflecting agronomic reality rather than distribution of land among HH. Comfortable land holdings in some villages are the mode, the most commonly encountered size land holdings. But in many villages most farmers have more than adequate land (wealthy), though usually not enormous holdings. Then there are the few holders of family land trusts with very little family to bequeath it to. Because the size of land holdings tends to cluster, switching from one cluster to another requires more of a jump (in this case more of a loss) than moving up or down one or two HH in the ranking of per capita land holdings. Very few wealthy land holders have given up

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    EMP 2008 Annual Individual Report on Vulnerability Page iv enough land to become Non-Viable or Marginal, much less just “Comfortable”; the jump from Comfortable to Non-Viable or Marginal occurs more frequently. And, as mentioned, the slide from Marginal to Non-Viable is the most frequent. The following tables show the extent to which Project land acquisition has changed the land holding status of compensated HH in the 9 villages. Definition of Land Holding Status as Cordes per HHM Zero No land holdings Non-Viable Less than 2/3 corde per HHM or 0.67 corde per HHM Marginal 0.68 to 1 corde per HHM Comfortable 1 to 2.5 corde per HHM Wealthy 2.5 cordes per HHM and above Sub-categories of “Wealthy” 2.5 to 10 or 11 cordes per HHM 12-13 to 19 cordes per HHM 20 to 30-31 cordes per HHM Above The next table will show that the Project has changed the land holding status of about 177 HH in the 9 surveyed villages. In a few instances other factors have intervened to bring about a status change, e.g. the young male HH who was Non-Viable until he inherited 30 cordes after his father’s death. To make a comparison easier, the second following table shows these changes as a percent of all the HH in the village. When the change in status (whatever the cause -- the Project, inheritance, divorce) is averaged across the 9 villages, 12% of HH have changed status. 3 % have become Non-Viable and 5% Marginal due to the Project or other factors. 8% of HH have become at risk since the start of the Project, and from the time of their first compensation.

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    EMP 2008 Annual Individual Report on Vulnerability Page i Change in Status Begada Bela Danmadja Dildo Dokaidilti Madjo Bero Mbanga Mouarom Ngalaba nonviable to zero 2 0 1 1 0 2 0 2 0 marginal to zero 0 0 0 0 0 0 0 0 0 marginal to nonviable 2 2 3 4 2 3 3 0 4 comfortable to nonviable 3 1 1 1 1 1 1 1 1 wealthy to nonviable 0 0 0 1 0 0 0 0 0 comfortable to marginal 6 0 15 8 7 4 6 3 7 wealthy to marginal 0 0 0 0 0 0 0 0 0 wealthy to comfortable 14 3 7 3 1 10 15 6 19 Change in Status= Overall Social Impact of Project 27 6 27 18 11 20 25 12 31 Nov 09 nonviable HH 7 3 5 7 3 6 4 3 5 Nov 09 marginal HH 6 0 15 8 7 4 6 3 7 Total At-Risk 13 3 20 15 10 10 10 6 12

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    EMP 2008 Annual Individual Report on Vulnerability Page ii % of Village HH that Changed in Status Begada Bela Danmadja Dildo Dokaidilti Madjo Bero Mbanga Mouarom Ngalaba % nonviable to zero 1 0 1 0 0 2 0 2 0 % marginal to zero 0 0 0 0 0 0 0 0 0 % marginal to nonviable 1 1 3 1 2 2 1 0 2 % comfortable to nonviable 1 1 1 0 1 1 0 1 0 % wealthy to nonviable 0 0 0 0 0 0 0 0 0 % comfortable to marginal 2 0 15 3 8 3 2 4 3 % wealthy to marginal 0 0 70 0 0 0 0 0 0 % wealthy to comfortable 5 2 7 1 1 8 6 7 8 Overall Social Impact of Project : % 10 4 27 7 13 15 9 14 12 % Nov 09 nonviable HH 3 2 5 3 4 5 1 4 2 % Nov 09 marginal HH 2 0 15 3 8 3 2 4 3 % Total At-Risk 5 2 20 6 12 8 3 8 5

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    EMP 2008 Annual Individual Report on Vulnerability Page i These tables indicate that overall project impact, bringing about a change in HH land holding status, has been: • Highest on Danmadja • Moderate on Begada, Dokaidilti, Madjo Bero, Mouarom and Ngalaba • Low on Bela, Dildo, and Mbanga 5. Danmadja: Village and HH Resources Interact Danmadja’s distinctiveness among the other 9 villages has been previously mentioned in terms of length of fallow possible (Danmadja is almost at the brink of no longer being able to fallow land sufficiently); its high population density; and the large number of HH that have changed land holding status. In Danmadja the same proportion of HH received compensation as in most other villages; it resembles the other villages in age and sex distribution, number of HHH by sex and by other criteria. The main difference between Danmadja and the others is that the population increase between 1993 and 2000 was far greater than in the other villages. And again, although like the other villages its increase between 2000 and 2009 was less than in the preceding years; it was still the highest during the Project period of all the 9 surveyed. In Danmadja there are now fewer productive agricultural resources per HH and per capita than elsewhere. Fishing provides an alternative resource but even so its land resources are far more strained than any of the other fishing villages. Danmadja’s distinctiveness is made clear in the following table: Color code: Low Medium High HH Size Smaller Med Low Med High High Pop Density/ Ha Low Medium High Length Fallow More than adequate Adequate Barely adequate HH Change in Status Few HH change Mod HH change High change

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    EMP 2008 Annual Individual Report on Vulnerability Page ii Fishing Pop per Length Any Change Villages Population # HH HH Size Ha fallow Land Status 570 101 5.6 1.19 6 27% Danmadja 1 325 268 4.9 0.71 12 7% Dildo 533 85 6.3 0.78 10 13% Dokaidilti 839 131 6.4 0.39 26 15% Madjo Pop per Length Any Change Inland Population # HH HH Size Ha fallow Land Status Villages 1 287 259 5.0 0.36 26 10% Begada 1 497 269 5.6 0.49 26 9% Mbanga 444 84 5.3 0.33 32 14% Mouarom 830 144 5.8 0.38 27 4% Bela 1 318 249 5.3 0.63 15 12% Ngalaba This table shows what has been seen above: there is little/no relation between HH size and other village characteristics. (The fishing villages have the largest or larger HH size but there is, at the current level of ethnographic analysis, no social reason for this and it may be simply a demographic difference.) Danmadja, which is short on fallow and dense in population, experienced a much larger shift in HH changing from one landholding status to another. The subtraction of small amounts of land (the typical size of land acquisitions) has had the most impact in Danmadja where more HH are smaller land holders than in the other villages. Landholding Danmadja % HH Avg. % of Villages Comment on Danmadja vs Status Landholding Status Landholding Status Avg. of other villages Non-Viable 16 10 Highest of all villages Marginal 21 10 Highest of all villages Comfortable 39 35 Equivalent of all villages Wealthy 25 45 Lowest of all villages

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    EMP 2008 Annual Individual Report on Vulnerability Page iii # All Danmadja HH at Factor Per Capita Male HHH Female HHH HH viability factor Total # current HH 102 before now before now zero 1 0 0 0 1 <2/3 15 10 13 2 2 <1 21 7 16 2 5 <2.5 40 41 35 8 5 2.5 + 25 30 23 2 2 # Compensated Danmadja HH at Factor Per Capita Total # current Male HHH Female HHH HH viability factor HH 88 before now before now zero 1 0 0 0 1 <2/3 12 8 11 2 1 <1 17 5 14 0 3 <2.5 37 38 32 8 5 2.5 + 21 27 20 1 1 A comparison of the graphs on per capita land holdings and per HH land holdings in Annexes 1 and 2 illustrate the same heavy distribution of Danmadja HH in small landholder categories. So far Danmadja has been coping with its land pressure through traditional mechanisms. The maps in Annex 3 show that Danmadja is already cultivating a large amount of land outside its village boundaries, especially on Mouarom land. This provides some relief to households because the hectares per person density on Danmadja land alone is only 0.68 whereas when the fields outside the village boundaries are added in the hectares increase to 0.85 ha/person. But no more expansion into Mouarom is possible; Danmadja fields are already abutting up against other villages’ fields, as the maps show. Danmadja has followed the traditional village split, which has effectively occurred though not yet officially recognized. As with other villages that have split earlier, the two halves are now mirror images.

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    EMP 2008 Annual Individual Report on Vulnerability Page iv Tot a l Popu la t ion = 5 7 0 D a nm a dj a 1 D a n m a dj a 2 Populat ion 369 210 % All Danm adj a Populat ion 65% 37% Pop Densit y people/ cde 1.75 2.07 # HH 61 41 % all Danm adj a HH 60% 40% Average Age HHH 39.0 39.4 Average # HHM 6.05 5.12 Average cde/ HH 11 11 Average cde/ capit a 2 2 So Danmadja has already maximized its ability to spread into a less densely used part of the village. The Project has offered, through Improved Agriculture training, the possibility for eligible farmers to learn vegetable farming on land bordering the river and this has become quite popular. The farmers sell their produce at Kome Atan market, earning considerable revenue. There is still land for gardens to expand and the market is not yet saturated. At this point Danmadja appears to be moving onto Bela village land to its east for rainfed farming. Bela is the village with the least land pressure of the 9. The map of Bela land farmed by others shows that some of the farmers with the least land in Danmadja have already made this move. However Bela is currently farming the area closest to its border with Danmadja while the areas closer to Bela’s settlement are in fallow. Danmadja might also choose to divide itself on both sides of the Nya River, similar to Madjo’s separation. Should it be the case that Danmadja has reached the limits of traditional mechanisms’ capacity to adjust population to land, any future land acquisition by the Project will need to reinforce dry season revenue earning activities or depend on physical resettlement. Should the pilot project for farmers to develop additional rice areas succeed, Danmadja will be one of the prime beneficiaries. 6. Conclusions on Village Surveys Village Surveys have presented a village-wide and, where neighbouring villages have also been mapped, an area-wide view of the distribution of productive assets. The Surveys have made clear that a geographical area taken as a whole is not undergoing major negative impacts because of the Project. Only a small portion of village households have felt negative

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