metadata: authors: 0: author: Drechsler,Mareile Beate Stephanie
1: author: Soer,Wolter
count: Ethiopia
docna: 0: docna: Early warning, early action : the use of
            predictive tools in drought response through Ethiopia's
            productive safety net programme
docty: Policy Research Working Paper
subtopic: Labor Markets,Hazard Risk Management,Nutrition,Hydrology,Social Risk Management,Food Security,Natural Disasters,Disaster Management
teratopic: Poverty Reduction,Conflict and Development,Communities and Human Settlements,Governance,Urban Development,Social Development,Macroeconomics and Economic Growth,Public Sector Development
abstracts: cdata!: This paper investigates the use of early
            warning tools as part of Ethiopia's Disaster Risk
            Management framework. Analyzing, in particular, the
            Livelihoods, Early Assessment and Protection tool,
            Livelihood Integrated Assessment and Hotspots Assessments,
            the paper delineates the scope and objectives of existing
            early warning tools, their commonalities and limitations.
            From a disaster risk financing and insurance perspective,
            the paper investigates possible enhancements in the existing
            early warning framework and its use that could facilitate
            greater timeliness of drought response. The paper argues
            that based on the existing early warning instruments and
            continued improvements to the early warning systems, it is
            possible to enable early action during the onset of a drought.
geo_reg_key: 517191
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            predictive tools in drought response through Ethiopia's
            productive safety net programme
display_title: Early warning, early action : the
            use of predictive tools in drought response through
            Ethiopia's productive safety net programme
listing_relative_url: /research/2016/06/26509999/early-warning-early-action-use-predictive-tools-drought-response-through-ethiopias-productive-safety-net-programme
topicv3: Food Security m1326439 199,Water m1326522 137,Disaster Risk Management m1326682 820,Drought m1503313 3162,Livelihoods m1583202 4425
publishtoextweb_dt: 2016-06-21T00:00:00Z
historic_topic: Poverty Reduction,Conflict and Development,Communities and Human Settlements,Governance,Urban Development,Social Development,Macroeconomics and Economic Growth,Public Sector Development
no_of_pages: 42
author_keys: 0: author_key: 1297807
1: author_key: 888945
model_extractions: WPS7716 Policy Research Working Paper 7716

# Early Warning, Early Action

The Use of Predictive Tools in Drought Response through Ethiopia’s Productive Safety Net Programme _Mareile Drechsler Wolter Soer_ Finance and Markets Global Practice Group June 2016
Policy Research Working Paper 7716

# **Abstract**

This paper investigates the use of early warning tools as part of Ethiopia’s Disaster Risk Management framework. Analyzing, in particular, the Livelihoods, Early Assessment and Protection tool, Livelihood Integrated Assessment and Hotspots Assessments, the paper delineates the scope and objectives of existing early warning tools, their commonalities and limitations. From a disaster risk financing and insurance perspective, the paper investigates possible enhancements in the existing early warning framework and its use that could facilitate greater timeliness of drought response. The paper argues that based on the existing early warning instruments and continued improvements to the early warning systems, it is possible to enable early action during the onset of a drought.

This paper is a product of the Disaster Risk Financing and Insurance Program (DRFIP), a partnership of the World Bank’s Finance and Markets Global Practice Group and the Global Facility for Disaster Reduction and Recovery, with funding from the UK Department for International Development. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at mdrechsler@ worldbank.org and wsoer@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# Early Warning, Early Action: The Use of Predictive Tools in Drought Response through Ethiopia’s Productive Safety Net Programme

Mareile Drechsler<sup>1</sup> and Wolter Soer<sup>2</sup>

**JEL codes:** H84 Disaster Aid, Q54 Climate, Natural Disasters and Their Management, Global Warming, I38 Government Policy, Provision and Effects of Welfare Programs

**Keywords:** Early Warning, Disaster Risk Management, Disaster Risk Financing and Insurance, Productive Safety Net Programme, Shocks and Vulnerability to Poverty

This paper is a product of the Disaster Risk Financing and Insurance Program (DRFIP), a partnership of the World Bank’s Finance and Markets Global Practice Group and the Global Facility for Disaster Reduction and Recovery, with funding from the UK Department for International Development. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org.

Many thanks, first and foremost, to Ato Tadesse Bekele, Engida Mena and Zinet Ahmet at the National Disaster Risk Management Commission (NDRMC) for their support of this project. Thanks also to John Aylieff, Samir Wanmali, Alemtsehai Alemu and Ezgimelese Tecleab at the World Food Programme (WFP) for their cooperation, provision of data and instructive comments. Stephen Anderson and Mark Lawrence at the Food Economy Group (FEG) provided helpful background information regarding the HEA methodology and LIAS tool. Many thanks are also due to Zerihun Mekuria at FEWS NET, who shared LIAS data used in this analysis and provided key background information on the sources and uses of LIAS data. Catherine Porter (Heriot-Watt University) conducted economic analyses and simulations of indicative drought-related poverty used in this paper. Background data and information were provided by Kelly Johnson, Segen Tewelde, Adeyabeba Mitiku Beyene, Elizabeth Farmer and Laura Campbell. Many thanks to Daniel Clarke and Ruth Hill, who have been very generous with their time and greatly helped in shaping this paper through many instructive discussions. Finally, many thanks to Richard Choularton (WFP) and Sarah Coll-Black (World Bank) for the provision of peer reviewer comments.

1 Finance and Markets Global Practice, World Bank Group, Washington DC, USA. E-mail: mdrechsler@worldbank.org.

2 Social Protection and Labor Global Practice, World Bank Group, Washington DC, USA. E-mail: wsoer@worldbank.org.
# Table of Contents

|I.<br>Introduction ...................................................................................................................................... 1|
|---|
|II.<br>Policy and institutional background.................................................................................................. 3|
|III.<br>Drought response within Ethiopia ................................................................................................ 4|
|III.1<br>The Productive Safety Net Programme (PSNP) ......................................................................... 4|
|III.2<br>Seasonal needs assessments and HRD process ........................................................................ 5|
|III.3<br>Risk layering through the Continuum of Response .................................................................. 8|
|IV.<br>Early warning systems ................................................................................................................. 10|
|IV.1.<br>The LEAP early warning tool ............................................................................................... 10|
|IV.1.1<br>The LEAP tool methodology ............................................................................................ 10|
|IV.1.2<br>Planned extensions of the LEAP tool .............................................................................. 12|
|IV.2<br>The LIAS tool ........................................................................................................................... 13|
|IV.2.1<br>The LIAS tool methodology ............................................................................................. 13|
|IV.2.2<br>Collection and use of LIAS data ....................................................................................... 15|
|IV.3<br>Complementarities between the LEAP and LIAS tools ........................................................... 16|
|IV.4<br>Bottom-up Early Warning System ........................................................................................... 17|
|IV.5<br>Hotspots assessments ............................................................................................................. 17|
|IV.6<br>Integrated Food Security Phase Classification ........................................................................ 18|
|IV.7<br>Potential for use of early warning tools to inform early action .............................................. 19|
|V.<br>Timing of early warning and drought response .............................................................................. 20|
|V.1<br>Timeline of drought impacts and early warning information ................................................. 20|
|V.2<br>The welfare benefits of a timely drought response ................................................................ 23|
|VI.<br>Discussion and conclusion .......................................................................................................... 24|
|References .............................................................................................................................................. 27|
|Annex 1: Data Sources Used and Computation of Beneficiary Numbers ............................................... 30|
|Data sources ........................................................................................................................................ 30|
|Process to match data sources ........................................................................................................... 32|
|Annex 2: The use of sovereign disaster risk financing instruments in a risk layering approach ............ 33<br>Annex 3: Regional Breakdown of LEAP and LIAS Data ............................................................................ 35|
# Acronyms

|BPR|Business Process Re-engineering|
|---|---|
|DP|Development Partner|
|DPPA|Disaster Prevention and Preparedness Agency|
|DPPC|Disaster Prevention and Preparedness Commission|
|DRM|Disaster Risk Management|
|DRMFSS|Disaster Risk Management and Food Security Sector|
|DRM SPIF|Disaster Risk Management Strategic Programme and Investment Framework|
|DRMTWG|Disaster Risk Management Technical Working Group|
|EWRD|Early Warning Response Directorate|
|EWS|Early Warning System|
|FCB|Federal Contingency Budget|
|FDRE|Federal Democratic Republic of Ethiopia|
|FEG|Food Economy Group|
|FEWS NET|Famine Early Warning Systems Network|
|FSCB|Food Security Coordination Bureau|
|FSCD|Food Security Coordination Directorate|
|GoE|Government of Ethiopia|
|GTP|Growth and Transformation Plan|
|HEA|Household Economy Approach|
|HEP|Health Extension Program|
|HICES|Household Income and Expenditure Survey|
|HRD|Humanitarian Response Document|
|IFPRI|International Food Policy Research Institute|
|LEAP|Livelihoods, Early Assessment and Protection|
|LIAS|Livelihood Impact Assessment Sheet|
|LIU|Livelihood Integration Unit|
|MoANR|Ministry of Agriculture and Natural Resources (former Ministry of Agriculture<br>and Rural Development (MoARD))|
|MoFEC|Ministry of Finance and Economic Cooperation (former Ministry of Finance<br>and Economic Development (MoFED))|
|---|---|
|NDRMC|National Disaster Risk Management Commission|
|NGO|Non-Governmental Organization|
|NMA|Ethiopia’s National Meteorological Agency|
|NPDPM|National Policy on Disaster Prevention and Management (1993)|
|NPDRM|National Policy and Strategy on Disaster Risk Management (2013)|
|PAD|Project Appraisal Document|
|PIM|Project Implementation Manual|
|PSNP|Productive Safety Net Programme|
|RFM|Risk Financing Mechanism|
|SNNPR|Southern Nations, Nationalities and Peoples’ Region|
|TLU|Tropical Livestock Units|
|TSF|Targeted Supplementary Feeding|
|UN|United Nations|
|WFP|World Food Programme|
|WMS|Welfare Monitoring Survey|
|WRSI|Water Requirement Satisfaction Index|
# I. Introduction

Ethiopia is frequently and severely affected by drought, with 70%<sup>1</sup> of the Ethiopian population at risk of disasters and climatic variability. This is epitomized by the 2011 drought in the Horn of Africa, which affected more than 12 million<sup>2</sup> people across Ethiopia, Somalia, Djibouti and Kenya. Droughts are associated with high economic costs, reducing Ethiopia's GDP by 1% to 4% in major event years<sup>3</sup> , causing a drag on economic growth and slackening the speed with which the poor can be lifted out of poverty. Today, approximately 42%<sup>4</sup> of GDP is generated through agricultural production and 85%<sup>5</sup> of the population is employed in the sector, resulting in the vulnerability of many Ethiopians to hydrometeorological hazards such as drought. Severe droughts occurring every three to five years cause crop loss and the starvation of livestock, leading to food insecurity among the affected population and jeopardizing the lives and livelihoods of many Ethiopians. Since 2000, approximately 6.2 million people have been affected by climatological hazards every year<sup>6</sup> , leading to the issuance of humanitarian appeals for assistance to an average of 2.5 million people annually<sup>7</sup> who suffer from food insecurity.

In light of the large adverse impacts of drought within Ethiopia, the Government of Ethiopia (GoE) has developed jointly with its Development Partners (DPs) early warning systems (EWS) that permit a timely recognition of impending droughts. EWS that are compiled regularly include, inter alia, the Livelihoods, Early Assessment and Protection (LEAP) tool developed in 2008 by the GoE and the World Food Programme (WFP), the Livelihood Impact Analysis Sheet (LIAS) developed in 2008 by the GoE and USAID, hotspots assessments conducted by the Early Warning and Response Directorate (EWRD) as well as other ad hoc assessments to determine the need for assistance in specific areas within Ethiopia.

EWS are used to feed into Ethiopia’s well-established drought response mechanisms, notably the Productive Safety Net Programme (PSNP) and humanitarian appeals, by means of which assistance is provided to the affected populations. The PSNP, which was developed by the GoE and its DPs and launched in 2005, has provided assistance to almost 8 million chronically food insecure households in 2015, of which 6.6 million beneficiaries were participating in public works activities, and 1.4 million labor-poor beneficiaries received direct support<sup>8</sup> . Since 2008, the PSNP has a drought response mechanism<sup>9</sup> , which permits the rapid scale-up<sup>10</sup> of the PSNP to include additional beneficiaries that are pushed into transitory food insecurity due to drought, and to extend the duration of payments made to 1 World Bank (2014b).

2 UN OCHA (2011).

> 3 OECD (2014).

> 4 World Bank, World Development Indicators.

> 5 CIA World Fact Book, https://www.cia.gov/library/publications/the-world-factbook/geos/et.html.

> 6 Based on Guha-Sapir, Below and Hoyois, EM-DAT database. Average number of people affected by drought based on EM-DAT data, taking into account droughts in 2003, 2005, 2008, 2009, 2011 and 2015.

7 Based on HRD Appeals for the years 2005 to 2014 (average beneficiaries across the assessments for the main harvesting seasons _belg_ and _meher_ ).

8 FDRE Ministry of Agriculture and Natural Resources, Rural Job Opportunity Creation and Food Security Sector, Food Security Coordination Directorate (2016).

9 Under PSNP III, drought response was managed through the Risk Financing Mechanism (RFM). Under PSNP IV, from July 2015 onwards, the RFM has been replaced with the Federal Contingency Budget (FCB).

10 In this paper, the term scalability / scale-up designates the inclusion of additional beneficiaries and extension of payments to existing beneficiaries in response to drought. While the FCB can be disbursed also in non-PSNP districts ( _woredas_ ) within PSNP regions, the term scale-up does not refer to the geographic expansion of PSNP coverage beyond current PSNP regions.

1
existing PSNP beneficiaries. In particular, the PSNP successfully scaled up during the Horn of Africa drought in 2011, supporting an additional 3.1 m beneficiaries for 3 months, and extending the duration of transfers for 6.5<sup>11</sup> million of the existing 7.6 million beneficiaries<sup>12</sup> . The PSNP’s response to the drought occurred within two months, contrasting with a time lag between the availability of early warning information and a response based on HRD appeals of up to eight months<sup>13</sup> . The PSNP’s response to the 2011 drought was widely credited with preventing the worst impacts of the drought, leading to comparatively less severe drought impacts within Ethiopia relative to its neighboring countries<sup>14</sup> .

Due to the high frequency of droughts in Ethiopia and concomitantly large number of food insecure households, drought response also relies on the issuance of humanitarian appeals to cover those needs that are not met through the PSNP. The Humanitarian Requirements Document (HRD) is developed by the GoE on the basis of bi-annual needs assessments following the main harvest seasons _meher_ and _belg_<sup>_15_</sup> _._ The Government regularly issues emergency appeals requesting emergency assistance to secure the consumption needs of vulnerable people due to the continued negative effects of the failure of _meher_ and _belg_ rains. PSNP contingency budgets are typically already used earlier in the year to provide a rapid response. While Ethiopia’s 2016 US$1.4 billion appeal has received over US$758 million from the Ethiopian government and the international community, leading to a coverage of the appeal by 54%, significant life-saving gaps remain across all sectors<sup>16</sup> .

Responding to drought effectively has been a priority within the GoE, and has been advanced at the policy and institutional level as well as through the establishment of the current drought response framework. Progress is promoted, in particular, through Ethiopia’s 2013 National Policy and Strategy on Disaster Risk Management (NPDRM), which looks to further enhance Ethiopia’s resilience against natural hazards and to mitigate the harmful effects of droughts. In line with these objectives, a key factor in protecting household welfare from the impacts of drought shocks is the timeliness of drought response. In particular, research by the International Food Policy Research Institute (IFPRI)<sup>17</sup> has shown that responding early during the onset of a drought can be up to three times as cost-effective as responding late.

The availability and use of robust, accurate predictive tools can greatly enhance the timeliness of drought response, facilitating early action during the onset of a drought. It is the objective of this paper to provide an overview of the existing EWS in Ethiopia, to delineate connections between different EWS, with a view to identifying possible enhancements in the existing drought response framework that could lead to increased timeliness of drought response. The paper will argue that an early intervention is less costly in terms of protecting lives and livelihoods than a late intervention, and can be achieved through greater – or renewed – use of existing early warning tools. The paper will argue furthermore that the

> 11 Slater and Bhuvanendra (2014).

> 12 FDRE Ministry of Agriculture, Disaster Risk Management and Food Security Sector, Food Security Coordination Directorate (2011).

> 13 Hobson and Campbell (2012).

> 14 World Bank (2011).

> 15 Throughout the year, three to four government-led multi-agency needs assessments are conducted (see Haan, Majid and Darcy, 2006).

> 16 UN OCHA (2016).

> 17 Clarke and Vargas Hill (2012).

2
existing EWS that have been developed in Ethiopia can be used to inform early action, and based on the continued collection of EWS data and continued improvements to the corresponding tools, there is scope for using EWS to inform an early and timely drought response.

The paper is structured as follows: section 2 will present the policy and institutional background for Disaster Risk Management (DRM) in Ethiopia. Section 3 will provide an overview of the existing drought response framework within Ethiopia. Section 4 presents existing early warning tools in Ethiopia, in particular, the LEAP and LIAS tools, as well as the GoE’s bottom-up early warning framework, hotspots assessments and the use of the Integrated Food Security Phase Classification. Section 5 investigates the timing of the availability of early warning information, as opposed to the HRD appeal process, and links this to the Ethiopian seasons. It discusses also the welfare benefits of an early drought response. Section 6 discusses and concludes.

# II. Policy and institutional background

Recognizing the need to systematically address drought risks and to protect Ethiopians from the loss of lives, livelihoods and income, the GoE has taken significant steps to systematically manage disaster risks. Efforts to manage drought risks date back to 1974, when the Relief and Rehabilitation Commission (RRC) was established in the aftermath of the severe 1973 drought. In 1995, the RRC was transformed into the Disaster Prevention and Preparedness Commission (DPPC). The strategic oversight of DRM in Ethiopia was vested in the National Disaster Prevention and Preparedness Committee (NDPPC), with the DPPC acting as NDPPC’s Secretariat to implement DRM policies and decisions. In 2004, DPPC was renamed the Disaster Prevention and Preparedness Agency (DPPA) and given narrower responsibilities to focus on emergency response, while a new Food Security Coordination Bureau (FSCB) was created to address issues of chronic food insecurity<sup>18</sup> .

In 2007, as part of a government-wide Business Process Re-engineering (BPR), the GoE transferred the responsibilities of the DPPA to the Ministry of Agriculture and Rural Development (MoARD<sup>19</sup> ), which led the establishment of the Disaster Risk Management and Food Security Sector (DRMFSS). The responsibilities for strategic oversight of DRM within Ethiopia were vested in DRMFSS, and its responsibilities included coordinating DRM activities across line ministries involved in integrating DRM at the sectoral level. DRMFSS was composed of the Early Warning and Response Directorate (EWRD) and the Food Security Coordination Directorate (FSCD). The institutional landscape for DRM in Ethiopia has recently undergone further change, with the creation of the National Disaster Risk Management Commission (NDRMC) as an autonomous federal government office accountable to the Prime Minister. The role of NDRMC is to lead the implementation of the National Disaster Risk Management Policy. The responsibilities vested in DRMFSS have been separated, such that the EWRD has been included in the new NDRMC and the FSCD forms part of the MoANR under the new Rural Job Creation and Food Security sector.

At the policy level, the GoE has developed a National Policy and Strategy on Disaster Risk Management (NPDRM) in 2013, and has recently developed the Disaster Risk Management Strategic Programme and Investment Framework (DRM-SPIF). Key goals of the NPDRM include the enhancement of Ethiopia’s

> 18 World Bank and GFDRR (2011).

> 19 In 2015, the Ministry of Agriculture and Rural Development was renamed Ministry of Agriculture and Natural Resources (MoANR), with a separate ministry created for the livestock and fisheries sectors, which are now governed by the Ministry of Livestock and Fisheries (MoLF).

3
capacity to withstand the impact of natural hazards at the national, local, community and household level, and to significantly reduce the damages associated with disasters by 2023. The NPDRM is organized around the Hyogo Framework for Action’s priority areas and promotes an integrated, multisectoral approach in the context of broader sustainable development efforts in Ethiopia. The DRM-SPIF is a tool to facilitate the National Policy and Strategy on DRM by addressing existing gaps and limitations in Ethiopia’s DRM capacity and establishing an integrated DRM system. The DRM-SPIF is also envisaged to contribute to Ethiopia’s ambitious Growth and Transformation Plan II (GTP II<sup>20</sup> ), which sets forth goals to enable Ethiopia to become a middle-income, carbon-neutral, and climate-resilient economy by 2025 with growth rates of at least 11 percent per annum during the planning period. To achieve the GTP II goals and objectives, GoE has followed a “developmental state” model with a strong role for the government in many aspects of the economy. It has prioritized key sectors such as industry and agriculture as drivers of sustained economic growth and job creation<sup>21</sup> .

# III. Drought response within Ethiopia

Against the policy and institutional backdrop outlined above, Ethiopia currently relies mainly on the Productive Safety Net Programme (PSNP) and humanitarian appeals to address drought risks<sup>22</sup> . The need to develop systems and institutions to respond to drought has been recognized in Ethiopia at least since the 1970s and 1980s, when two major droughts caused widespread food insecurity and mortality. The catastrophic drought of 1983 caused 300,000 deaths and affected 7.75 million Ethiopians<sup>23</sup> . The severity of the crisis necessitated large-scale international humanitarian assistance, underscoring the importance of establishing a sound drought response framework. To manage drought risks systematically, in 2005 the GoE began implementing jointly with its DPs the Productive Safety Net Programme to respond to food insecurity and droughts, and has established a seasonal needs assessment process to monitor food security and issue humanitarian appeals where necessary.

## III.1 The Productive Safety Net Programme (PSNP)

The PSNP represents the second-largest social safety net in Sub-Saharan Africa<sup>24</sup> , and provides cash or food assistance, depending on availability of food in the market, to chronically food insecure households in chronically food insecure _woredas_ (districts). Since 2005, transfers have been made for six months in a given calendar year, and are equivalent in value to 15 kg of cereals per household member per month, or its cash equivalent. Under the current PSNP IV, which commenced in 2015, the transfer value will be adapted to 15 kg of grain and 4 kg of pulses in order to increase the nutritional value of the transfer. Beneficiary selection within the PSNP is completed based on geographic and community targeting. Based on a history of emergency food needs, the GoE identified the most food insecure _woredas_ . As a second step, beneficiaries are selected within communities according to pre-identified criteria.

> 20 FDRE (2015).

> 21 World Bank (2014b).

> 22 Additionally to the PSNP and humanitarian appeals to address drought risks, the GoE’s GTP II includes objectives to enhance resilience to drought risks. These include, for instance, initiatives to strengthen natural resource management and watershed development, as well as scaling up best practices to help mitigate vulnerability to drought. Furthermore, the GTP II envisages reducing the vulnerability to drought risks through small and large scale irrigation development (FDRE, 2015).

> 23 Guha-Sapir, Below and Hoyois, EM-DAT database.

> 24 Following South Africa’s Child Support Grant, see World Bank (2014a).

4
Evaluations of the PSNP have shown that the program has significantly reduced food insecurity and distress sales of assets. Data collected for the 2014 PSNP highlands impact evaluation<sup>25</sup> show that food security has improved significantly in PSNP localities: the average PSNP beneficiary household reported a food gap of approximately 3 months between 2006 and 2010. This has dropped to 2.04 months in 2012 and 1.75 months in 2014. Moreover, the PSNP has led to a significant decrease in distress asset sales. While in 2010, 54% of public works beneficiary households made distress sales to meet food needs, by 2014, this has dropped to 25%. It has also been found that livestock holdings by the poorest PSNP public works participants have increased markedly, from 0.5 Tropical Livestock Units (TLU) in 2006 to 1.65 TLU in 2014.

Drought response through the PSNP is conducted through the use of the PSNP’s contingency budgets. Under the PSNP III program, which was in effect between 2009 and 2015, drought response was managed through the use of _woreda_ and regional contingency budgets, as well as the Risk Financing Mechanism (RFM). Thereby, _woreda_ contingency budgets were designed to address inclusion and exclusion errors<sup>26</sup> as well as unexpected needs of chronically food insecure households. Regional contingency budgets were designed to address unexpected needs of the chronically food insecure population, and transitory food insecurity. Regional contingency budgets were to be used prior to the release of funds under the RFM, unless the severity of the shock was beyond the capacity of regional budgets<sup>27</sup> . The PSNP III’s RFM was designed specifically to respond to drought shocks. In particular, the mechanism both extends the transfer amount to existing beneficiaries, and includes additional beneficiaries that suffer from food insecurity as a result of drought.

Under the PSNP IV, amendments have been made to the mechanisms to respond to drought. The PSNP IV came into effect in July 2015, and no longer includes a regional-level contingency budget, in order to have a more discretionary use of these resources in response to future shocks. Under PSNP III regional contingency budgets could be rolled forward to the next season, while under PSNP IV this is no longer possible. However, the program continues to use _woreda_ -level contingency budgets to address beneficiary inclusion and exclusion errors and minor transitory shocks. Major drought shocks are addressed, under PSNP IV, through its federal-level contingency budget, which has replaced the PSNP III’s RFM. Under PSNP IV, part of the core budget is allocated to scale up the activities of PSNP IV using the federal contingency budget (FCB) and in 2016 an amount of approximately US$ 50 m<sup>28</sup> was allocated for this purpose.

# III.2 Seasonal needs assessments and HRD process

Additionally to a response through the PSNP, drought risks within Ethiopia are typically addressed through the issuance of humanitarian appeals. Appeal numbers are determined based on a seasonal needs assessment conducted following the main harvesting seasons _meher_ and _belg_ .

> 25 See Berhane, Hirvonen and Hoddinott (2015).

> 26 An inclusion error may occur when a food secure household is erroneously included in the PSNP. An exclusion error would occur when a food insecure household is excluded from the PSNP. Inclusion and exclusion errors may arise for a diverse range of reasons, including a failure to review beneficiary lists on a regular basis at the _woreda_ level, migration or death of beneficiaries, misconceptions regarding the beneficiary selection criteria, and so forth. 27 See World Bank (2009).

> 28 This amount may be increased in the second half 2016 as part of the 2016/2017 annual PSNP plan.

5
HRD appeal numbers are based on a bottom-up assessment process and agreed assumptions of need, as follows (Source: Food Economy Group 2015):

- (i) **_Pre-harvest assessment triggered_** : The pre-harvest needs assessment is triggered by the Disaster Risk Management Technical Working Group (DRM TWG<sup>29</sup> ) and conducted by _woreda_ - level staff. Assessments are conducted within two weeks before or after each rainy season, depending on rainfall conditions.

- (ii) **_Pre-harvest assessment:_** _Woreda_ -level staff conduct an initial assessment of the number of people in need of food assistance. Assessments are based on a range of information, including rainfall and crop production data. _Woreda_ -level staff submit needs estimates to the zones, which revise and compile needs estimates. Zonal-level beneficiary estimates are submitted to regional governments, who compile and revise beneficiary estimates again.

- (iii) **_Main seasonal needs assessments:_** The main seasonal needs assessments are conducted by multi-agency teams dispatched by the federal government to the regions. In particular, assessments are conducted in specific _woredas_ . Agreement is reached on which _woredas_ to visit through coordination between assessment teams and the regional-level governments, based on pre-harvest assessment results. In determining beneficiary numbers, checklists are used to guide teams through the assessment process. Beneficiary numbers were calculated initially based on the Household Economy Approach (HEA, see section 4.2 for further details), and were computed using the LIAS spreadsheet. The spreadsheet produces, as a final output, the number of people in need of food assistance. Two different thresholds are used: A Livelihood Protection Threshold<sup>30</sup> , below which households are unable to meet food needs whilst upholding their livelihood strategy, and a Survival Threshold<sup>31</sup> , below which households are unable to meet food needs. As of 2015, the DRM TWG decided to discontinue the use of LIAS data, as it was recognized that LIAS baseline data are outdated for the highlands regions. At present, seasonal needs assessments are therefore mainly based on the use of checklists.

- (iv) **_Drafting of HRD Document:_** Based on compiled beneficiary numbers, the federal-level HRD Editorial Committee drafts the HRD document. The HRD Editorial Committee is composed of representatives of DRMFSS, the UN and NGOs. The Committee compiles the received assessments and presents these to the EWRD at DRMFSS.

29 The DRM Technical Working Group is a multi-sectoral forum led by DRMFSS, including GoE representatives, DPs and humanitarian partners.

30 The Livelihoods Protection Threshold represents the total income required to sustain local livelihoods, in particular: (i) ensuring basic survival, (ii) maintaining access to basic services, such as health and education expenses, (iii) sustaining livelihoods in the medium to longer term through purchases of seeds, fertilizer, veterinary drugs, etc., (iv) achieving a minimum locally acceptable standard of living through purchase of clothing, coffee and tea, etc. See USAID and FDRE Disaster Risk Management and Food Security Sector, MoARD: _The Livelihoods Integration Unit Uses of the Baseline Information and Analysis._ 31 The Survival Threshold represents the total income required to cover: (i) 100% of minimum food energy needs (2100 kcals per person), (ii) the costs associated with food preparation and consumption, such as salt, soap, kerosene, and/or firewood for cooking and basic lighting, (iii) any expenditure on water for human consumption. The Survival Threshold is the line below which intervention is required to save lives. See USAID and FDRE Disaster Risk Management and Food Security Sector, MoARD: _The Livelihoods Integration Unit Uses of the Baseline Information and Analysis._ 6
<!-- Start of picture text --> Pre-harvest assessment triggered<br>Pre-harvest assessment triggered by Disaster Risk Management Technical Working Group (DRM TWG) and<br>conducted by woreda-level staff<br>Pre-harvest assessment<br>Initial assessment of number of people in need of food assistance based on a range of information including crop<br>and rainfall data<br>Main seasonal needs assessment<br>Multi-agency teams dispatched by the federal government to the regions conduct the main seasonal assessment<br>Drafting ofHRD document<br>The federal-level HRD Editorial Committee drafts the HRD document based on beneficiary numbers<br>Review of draft HRD document by DRMFSS<br>DRMFSS confirms the summary provided by the HRD Editorial Committee with regional governments<br>Submission of beneficiary numbers to State Minister<br>DRMFSS submits beneficiary numbers to the State Minister for review<br>Final review by national DRM Council<br>Beneficiary numbers are reviewed by the National DRM Council<br>Finalization ofHRD<br>Beneficiary numbers are submitted to the HRD Editorial Committee and published in the Humanitarian<br>Requirements Document<br><!-- End of picture text -->
# III.3 Risk layering through the Continuum of Response

Drought-risk management within Ethiopia will be based on the consecutive use of the PSNP core budget, PSNP _woreda_ -level budgets, PSNP federal level contingency budgets and funds raised through the HRD appeal process. This consecutive process of using available funds is specified, within PSNP IV, in the Continuum of Response<sup>33</sup> summarized in Table 1. Note, in particular, that according to the PSNP IV Programme Implementation Manual (PIM), the PSNP FCB will be the first line of financing for response to transitory food needs in PSNP regions, and humanitarian resources will be used for food needs in PSNP regions only if the FCB is insufficient to meet the anticipated needs. Non-food requirements are met entirely by humanitarian resources.

TABLE 1: CONTINUUM OF RESPONSE

|**Funding Source and Purpose**|**Trigger**|**Where Resources**<br>**can be Used**|**Implementation**<br>**Responsibility**|
|---|---|---|---|
|**Woreda Contingency Budget**<br>||||
|To address exclusion error<br>identified through appeals<br>To address transitory needs<sup>34</sup><br>**Federal Risk Financing/Contin**<br>|<br>Appeals<br><br>Ongoing (improved)<br>Early Warning<sup>35</sup><br>**gency Budget**|Anywhere within<br>woreda where the<br>safety net is<br>implemented<br>|<br>Woreda Food Security<br>Desk (WFSD)|
|To address transitory needs|<br>Annual Needs<br>Assessment and other<br>hotspot assessments<br>(real time early<br>warning data)|In regions where<br>the safety net is<br>implemented|<br>In existing operational<br>areas – WFSD.<br><br>In non-operational<br>areas - WEWRD<sup>36</sup>and<br>other humanitarian<br>actors as appropriate|
|**Ad Hoc Humanitarian Respons**<br>|**e**<br>|||
|To address transitory needs|<br>Annual Needs<br>Assessment and other<br>hotspot assessments|Nationwide|<br>All actors with<br>operational capacity<br>(WFSD, WEWRD, WFP<br>and other UN actors<br>NGOs etc.)|

The use of resources according to the Continuum of Response can be linked to a risk layering approach (see Annex 2 for further details), whereby different risk financing instruments are made available depending on the frequency and severity of the disaster event. In particular, chronic poverty is addressed through the PSNP’s core caseload. High frequency, low severity droughts occurring on a biannual or annual basis in specific _woredas_ are addressed through the PSNP’s _woreda_ -level contingency

> 33 While the continuum of response has been agreed by government and PSNP stakeholders for some time in practice the use of humanitarian resources has been triggered before the contingency budgets have been fully utilized in all major recent drought events.

> 34 This budget can both be used to respond to (i) transitory needs among safety net clients (by increasing the number of months of support); and (ii) transitory needs among non-safety net clients (by adding them temporarily to the programme payroll).

> 35 These improvements will both focus on the simplification of the data needed to trigger response, but also the development of analytical capacity to use and make effective and rapid decisions on the basis of these data. This is in line with the DRM-SPIF’s agenda of ensuring that early warning and risk assessment tools are harmonized into one system to allow effective decision-making.

> 36 Woreda Early Warning and Response Directorate.

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<!-- Start of picture text --> Current Risk Layering<br>PSNP, Risk Financing and Humanitarian Appeals<br>25.00<br>= 20.00 a Ne eee<br>S<br>= 15.00<br>Oo<br>3<br>S<br>22 10.00 oo N<br>8 S00 enn= ~<br>0.00<br>2005 =.2006-~=Ss«(2007,-—'s«2008-~Ss«(2009.-S's«2010.—S-s«2011.—St's«2012—s=—«‘SSs«204<br>Year<br>—@— |ndicative poverty (100%) ——@=—= PSNP = @ = PSNP regular caseload + RFM —@=<= PSNP + RFM + HRD<br><!-- End of picture text -->
# IV. Early warning systems

Ethiopia has a range of EWS that permit the early recognition of the onset of a drought. These tools include, inter alia, the LEAP and LIAS tools, a bottom up early warning system, hotspots analyses and the Integrated Food Security Phase Classification<sup>39</sup> . The following sections will provide an overview of the methodologies used in each of the tools, with a view to assessing how the existing EWS can be used within Ethiopia’s drought response framework.

## IV.1.  The LEAP early warning tool

### _IV.1.1  The LEAP tool methodology_

LEAP<sup>40</sup> was initiated during PSNP I in 2006 and was developed to estimate food aid beneficiary numbers, and to develop indices for early response in terms of food security. Under PSNP III and IV respectively, the LEAP tool was chosen as a trigger to release PSNP funds under the RFM and FCB. Based on LEAP data indicating a drought of sufficient strength to trigger contingency funds, the PSNP would respond to drought in a timely manner. By providing early warning information during the onset of a drought, the LEAP tool facilitates early action, thereby mitigating the adverse impacts of droughts on food security.

The objective of the LEAP tool is to provide estimates of the number of people in need of food assistance during each of the main harvest seasons _meher_ and _belg_ , at the _woreda_ , regional and national scale. To obtain estimated beneficiary numbers, the LEAP tool performs the following steps (Source: Hoefsloot et al. 2013):

- (i) **_Calculation of planting date:_** Based on crop and rainfall data, the LEAP tool computes the estimated planting date for a range of crops. Thereby, planting dates are computed for each crop and geographic region individually, depending on current rainfall data. Planting dates are computed as a sowing window start dekad<sup>41</sup> and a sowing window end dekad. To determine the planting dekad within this window, LEAP identifies two consecutive dekads in which certain rainfall thresholds are exceeded (these thresholds are crop and location specific). This is usually a good approximation of farmers’ decision to start planting.

- (ii) **_Calculation of Water Requirements Satisfaction Index (WRSI)_**<sup>**_42_**</sup> **_:_** The computation of the WRSI is based on a number of input parameters. Specifically, these include rainfall, the planting start and end dates computed in (i), potential evapotranspiration, soil water holding capacity, crop type, the water use rates of a growing crop, the geographical area where the crop is grown, and the length of the growing period. All of these parameters are static except rainfall, which

39 Additionally to the LEAP and LIAS tools and hotspots assessments, a range of other early warning data are collected regularly in Ethiopia. These include, for instance, Integrated Food Security Phase Classification data are collected by FEWS NET (see section IV.7).Moreover, the European Commission’s Joint Research Centre (JRC) uses remote sensing to monitor agricultural and pastoral vegetation in the Horn of Africa on a real time basis.

40 Initially, the LEAP project included the LEAP tool, a contingency finance facility, contingency plans, and national capacity building activities. LEAP was initiated following the implementation of a sovereign index insurance pilot conducted by WFP and GoE. With time, LEAP focused increasingly on the development of the LEAP tool and provision of the tools intermediary and final outputs.

> 41 A dekad is a time interval of approximately ten days. Every month has three dekads, such that the first two dekads have 10 days, and the third is comprised of the remaining days of the month.

42 The Water Requirements Satisfaction Index (WRSI) is an indicator of crop performance based on the availability of water during a growing season. A WRSI value of 100% would indicate that water requirements for plant growth are fully satisfied.

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changes over time. Based on the WRSI for individual crops, LEAP permits the calculation of a basket WRSI reflecting the WRSI for a basket of crops grown in a given area.

- (iii) **_Calculation of yield reduction:_** As an intermediary output, the LEAP tool computes yield reduction estimates. A 0% yield reduction would reflect that the harvest would be at its potential, and a yield reduction of 100% would reflect a complete crop failure. Yield reduction is computed for each crop and each region, and is dependent on actual evapotranspiration, total water requirement without water stress and a yield response factor representing the effect of a reduction in evapotranspiration on yield losses<sup>43</sup> . Based on yield reduction estimates for individual crops, LEAP permits the calculation of yield reduction of a basket of crops grown in a given area.

- (iv) **_Calculation of estimated beneficiary numbers:_** Based on previously computed WRSI data in combination with past beneficiary numbers and demographic data, the LEAP tool computes estimated beneficiary numbers. In particular, the LEAP tool uses a logarithmic model to compute needs based on the following parameters:

- a. **_Needs in case of optimal rainfall_ :** Number of people in need of assistance in case of optimal rainfall in any given region. In particular, the level of needs in case of optimal rainfall may be interpreted as the number of chronically food insecure people, as their status of food insecurity is independent of crop performance.

- b. **_Population at risk of food insecurity_ :** This number corresponds to the maximum number of beneficiaries in the case of the most severe drought possible, and should include the entire population at risk of food insecurity due to drought in a given area. To optimally estimate this parameter, this variable should be based on field assessments to assess the number of people faced with drought-related food insecurity. However, in the absence of recently updated data, LEAP selects the maximum historical beneficiary number recorded in a given area.

- c. **_Lowest observed value of RWRSI:_** The Regional Water Requirements Satisfaction Index (RWRSI) is defined as the weighted average of woreda-level combined WRSI, where the weights are given by the population for each woreda. The lowest observed RWRSI reflects the lowest drought index based on historical data, reflecting the most severe drought on record in a given area. In cases where a drought occurs that is more severe than the most severe past drought, the model used is able to extrapolate the associated level of needs.

- d. **_Optimal RWRSI:_** The optimal RWRSI refers to the optimal rainfall conditions relative to a given region. Although a value of WRSI of 100% would reflect optimal crop growth conditions, the optimal RWRSI may be less than 100% depending on local conditions.

- e. **_Systemic failure level:_** This parameter estimates the complete failure of the crop seasons, reflecting the level of water stress beyond which community coping mechanisms are exhausted and beneficiaries require external assistance to meet food needs. The parameter is difficult to estimate, as it depends on farming practices and farmers’ skills. However, the failure level should be selected such that it is significantly lower than the lowest observed value of RWRSI (see c. above). One possibility for selecting the systemic failure level is to use a value of WRSI of 50, below which crops are assumed to fail, or to link the systemic failure level to the lowest observed value of RWRSI by scaling down the lowest observed value of RWRSI through a constant assumed to be the threshold beyond which crops fail.

> 43 Doorenbos and Kassam (1979).

11
<!-- Start of picture text --> (——Water Requirement . . .<br>Satisfaction Index (WRSI) Yield reduction estimates<br>Rainfall data<br>Evapotranspiration data<br>Past beneficiary numbers .<br>and demographics. Beneficiary numbers<br><!-- End of picture text -->
judgment to account for risk factors different than drought (Hoefsloot et al. 2013). In particular, as section III.2 showed, beneficiary numbers are subject to a detailed review process which can lead to changes in estimated beneficiary numbers. Given that historical beneficiary data are thus informed not only by current drought data but also by informed judgements, the LEAP tool is being extended to base beneficiary estimates on household livelihoods data (see section IV.2 below).

Due to the use of historic beneficiary numbers in computing needs, LEAP early warning information has so far not been used to inform insurance<sup>46</sup> . Upon the integration of household livelihood data to compute beneficiary numbers and hence elimination of subjective information in the computation of beneficiary numbers, the LEAP tool could serve as a basis for weather index insurance.

- (ii) **_Integration of market and price data:_** A further constraint of the LEAP tool in its current version is that the tool does not take into account market and price data. In particular, the ability of households to acquire food is determined not only by drought conditions (impacting on farmers’ ability to produce crops and livestock), but also by their ability to buy food in the market. Farmers’ ability to buy food is determined, for instance by the availability of food in the market, the distance of the household to the market, and food prices. To enhance the accuracy of beneficiary estimates obtained through the LEAP tool, the LEAP tool is being reviewed to include these variables.

- (iii) **_Integration of data for pastoral regions:_** Ethiopia is composed of a range of diverse livelihood zones, with livestock production representing a key source of income particularly in the regions of Afar, Borena and Somali. At present, the LEAP tool is centered mainly on developing beneficiary numbers for the crop seasons _meher_ and _belg_ . By integrating a pastoral index into the tool, LEAP could additionally generate beneficiary estimates for pastoral areas. GoE and WFP are currently in the process of extending the LEAP tool to integrate pastoral information.

- (iv) **_Flood monitoring:_** Additionally to drought, flood risks represent a major hazard in Ethiopia, with floods leading to average annual losses of US$ 200 million<sup>47</sup> . It is therefore envisaged that the LEAP tool could be enhanced by integrating a flood index, such that it could serve as a flood monitoring tool to generate beneficiary estimates for floods.

# IV.2 The LIAS tool

## _IV.2.1  The LIAS tool methodology_

The Livelihood Impact Analysis Sheet (LIAS) is an early warning tool that estimates beneficiary numbers based on the Household Economy Approach (HEA). LIAS was developed and collected initially through the Livelihood Integration Unit (LIU), which was established in 2006 as a USAID-funded project based in the former DPPA. The LIU was in operation until 2015, and was housed in the EWRD as part of DRMFSS. The objective of the LIU was to improve the accuracy and objectiveness of seasonal needs assessments in Ethiopia, by integrating an understanding of local livelihoods into the needs assessment process<sup>48</sup> .

> 46 At the micro-level, it is possible for LEAP data to be used to inform insurance. This would, however, require a detailed local-level index design process.

> 47 World Bank (2014b).

> 48 FDRE Disaster Risk Management & Food Security Sector, MoARD and USAID: The Livelihoods Integration Unit Uses of the Baseline Information and Analysis.

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The HEA is a method for assessing the vulnerability of rural populations to economic shocks and changes, based on their livelihood patterns and market information. It links together the concepts of risk, vulnerability and capacity in the context of livelihood security. This can be expressed in the following formula: Where R designates risk, h refers to hazard, v to vulnerability, c to coping and f(∙) is a function that maps hazard, vulnerability and coping to risk. Thereby, risk quantifies the risk of food or livelihood insecurity, hazard measures the shock a household experiences, vulnerability is a measure of how strongly any given shock would impact on a given household, and the coping capacity measures how well households are able to respond to a shock. The HEA method involves six steps to predict the number of people at risk of food and livelihood insecurity:

- (i) **_Livelihood zoning:_** Ethiopia comprises a diverse range of livelihood zones, with livelihoods varying depending on climate, soil, access to markets and crop or livestock production. The first step in the HEA method is to develop a livelihood zone map, which lays out zones within which people share similar patterns of access to foods (e.g. producing the same crops, keeping the same types of livestock, etc.) and have the same access to markets.

- (ii) **_Wealth breakdowns_** _:_ A key factor in the ability of a household to generate income and acquire food is wealth. Wealth is determined, for instance, by land ownership, livestock holdings, capital, education, skills, labor availability and social capital. As part of the second step of the HEA method, a wealth breakdown is developed, whereby within each livelihood zone, different wealth groups are defined. The output of the second step of the HEA method is a wealth breakdown.

- (iii) **_Quantification of livelihood strategies:_** Once households are grouped into livelihood zones and wealth categories, baseline data is generated. The baseline data shows the contribution of crops, livestock, and other sources to household’s food intake and cash income, on the assumption that the year is an average year. The baseline year serves as a comparison year.

- (iv) **_Hazard analysis:_** To understand how hazards such as drought risks affect the livelihood strategy of any given household, it is necessary to understand the economic consequences of any given hazard. In particular, the economic outcome is obtained by mathematically computing the impact of the hazard on livelihood baselines.

- (v) **_Analysis of coping capacity:_** To mitigate the impact of a shock, household can use coping strategies. Thereby, some coping strategies are harmful as they diminish the household’s asset base. Examples include unsustainable sale/slaughter of livestock, distress migration of households, excessive sale of firewood, or the sale of productive assets. Other coping strategies have a relatively low cost, such as, for instance, reduced expenditure on non-essential items, harvesting of reserve crops, or the consumption rather than sale of any crop surplus<sup>49</sup> . In this step, the extent to which households are able to cope with drought is analyzed, taking account only of low-cost coping strategies that do not reduce the household’s productive capacity in the long term.

> 49 Boudreau (2009).

14
- (vi) **_Outcome analysis:_** As a final outcome, the LIAS tool produces estimates of the number of people in need in any given _woreda_ . Thereby, two different thresholds are used: the livelihood protection threshold and the survival deficit. If total household income falls below the livelihood protection threshold, households are required to employ high-cost coping strategies to meet their food requirements. If household income falls below the survival deficit, they are unable to meet their food requirements even when livelihood-damaging coping strategies are used. The LIAS tool estimates beneficiary numbers by _woreda_ both for households that fall under the livelihood protection threshold, and for households falling below the survival threshold, based on the magnitude of the drought shock.

The HEA method is summarized in Table 2, illustrating how food security outcomes are linked to vulnerabilities, capabilities and hazard information.

||TABLE2: THEHEA METHOD. (RE<br> <br> <br>|PRODUCED FROMBOUDREAU200<br>|9).<br>|
|---|---|---|---|
||**f**<br>**(Vulnerabilities**<br>**/**<br>**Capabilities**|**Hazard)**|**=Risk**|
|**The LIU System**|**Livelihood Baselines**<br>Gathered through intensive<br>fieldwork once every five to<br>ten<br>years<br>(depending<br>on<br>changes<br>in<br>fundamental<br>economy) by highly trained<br>teams|**Hazard Analysis**<br>Hazard<br>information<br>is<br>gathered<br>during<br>the<br>seasonal assessments by<br>GoE, UN, NGO and other<br>staff; ongoing monitoring<br>(of prices especially) adds<br>to information base|**Outcome Analysis**<br>Conducted for seasonal<br>assessment and at other<br>times of year and for<br>other<br>purposes<br>as<br>required|

# _IV.2.2  Collection and use of LIAS data_

Within Ethiopia, LIAS data have been collected on a bi-annual basis for the two main harvesting seasons _mehe_ r and _belg_ . Following a pilot in 2008 in the region Southern Nations, Nationalities and Peoples (SNNPR), LIAS data have been collected throughout Ethiopia. In particular, the collection of LIAS data has formed part of the main seasonal needs assessments process, as described in step iii of section III.2 above. LIAS beneficiary estimates are conducted prior to the initiation of the review process to inform Humanitarian Appeal Numbers, and are developed using a clearly defined, transparent methodology. Due to these features, LIAS estimates represent a useful source of early warning information to inform drought response.

While the HEA methodology represents a key tool within Ethiopia’s early warning framework, the role of the LIAS tool within the seasonal needs assessment process has been subject to changes over time associated with changes to the definition of beneficiary numbers. In particular, during the years 2009 to 2010, beneficiary numbers reflected in the Humanitarian Requirements Document were based on initial LIAS estimates of food insecure households falling below the livelihood protection threshold, therefore including in the estimation of food insecure households those beneficiaries that have not made use of high-cost coping strategies. After 2010, the definition of beneficiary numbers reflected in the HRD was changed to reflect LIAS survival deficit numbers. The change of definition of beneficiary estimates led to lesser overall number of beneficiaries considered in the HRD process, as only those beneficiaries who were not able to meet food requirements even if damaging coping strategies were used were considered in humanitarian appeals. HRD beneficiary numbers therefore reflect the number of people at 15
risk of greater mortality as a result of drought; HRD beneficiaries are therefore also called emergency beneficiaries.

The collection of LIAS data has been impacted by changing ownership of the data collection process and capacity constraints with respect to the implementation of the HEA methodology. In particular, between 2008 and 2011, LIAS data were collected systematically in all _woredas_ . However, in 2011, the USAIDfunded LIU project closed, leading to changing ownership of the data collection process. While the LIU continued to be located within DRMFSS until 2015, data collection after 2011 has been less systematic, with LIAS data collected in some _woredas_ between 2011 and 2015 and not in others. A key constraint impeding the collection of LIAS data after 2011 has been a shortage of qualified technical staff. Moreover, a lack of familiarity with the HEA methodology among seasonal assessment teams has led to the increasing use of checklists and decreasing use of the HEA methodology in the determination of beneficiary numbers (for an overview of available LIAS data, refer to Annex 1). As of 2015, the use of LIAS data has been discontinued, as it was recognized that LIAS baseline data are outdated for the highlands regions.

The HEA methodology and LIAS tool have formed key inputs into the seasonal needs assessment process. Moreover, as LIAS data are collected in June and November respectively, which is approximately mid-way through the _meher_ and _belg_ harvest seasons, LIAS data may provide an early indication of drought conditions, allowing for a timely drought response. In the absence of the LIAS tool / HEA method, seasonal needs assessments are based mainly on checklists, leading to a less systematic and transparent data collection process. Recognizing the need for a renewed use of LIAS data, USAID has issued an RFP during 2015 to update LIAS baselines for the highlands regions. Work on updating baselines will be conducted during 2016, with a view to obtaining updated baselines for all regions in Ethiopia.

# IV.3 Complementarities between the LEAP and LIAS tools

The LEAP and LIAS tools complement each other in enabling a successful drought response. In particular, the two tools have a different focus, with the LEAP tool focused mainly on the impacts of a drought shock as measured by WRSI on HRD numbers, and the LIAS tool focused on assessing the implications of a range of variables – including market access and prices – on livelihood strategies. Each tool sheds light on the question of food security within Ethiopia from a different angle, with the LEAP tool providing a dynamic, macro-level assessment of drought risk, and the LIAS tool providing a detailed bottom-up view. It has therefore been proposed that LEAP and LIAS data should be integrated to a greater extent. For instance, LIAS data can be used within LEAP, as follows<sup>50</sup> :

- (i) To check planting dates and the length of crop cycles within LEAP;

- (ii) To determine where certain crops are grown and to adjust LEAP accordingly;

- (iii) To identify the areas where drought is likely to have the most severe impacts on livelihoods based on LIAS data on households’ dependence on self-grown crops for food;

- (iv) To support the development of a LEAP pastoral index based on LIAS data for pastoral areas.

> 50 Hoefsloot et al. 2013.

16
In combination, the results of the LEAP and LIAS tools can therefore provide a more detailed picture of drought and associated food insecurity in Ethiopia; this was proposed already early during the development of the LEAP tool in 2006.

# IV.4 Bottom-up Early Warning System

Recognizing the need to closely monitor droughts based on early warning data, the GoE has developed a bottom-up early warning framework through which monthly food security reports are produced. These are based on the use of checklists, as well as incident command systems. The bottom-up early warning system provides early warning data on a more continual, and less discrete, collection of early warning data, and could be used to replace the current seasonal needs assessment process. The bottom-up early warning systems could also be used to enable early action, as the gradual development of droughts is identifiable based on the collected data.

The successful deployment and maintenance of a bottom-up early warning system is likely to depend on strong implementation mechanisms at all levels of government, as well as the availability of technically qualified staff to collect and analyze early warning data. Moreover, to employ data collected through a bottom-up early warning process to inform early action, it is necessary to establish a clear connection between the collected data and drought-related need; this requires detailed data analysis, as well as a process to store and share data. Moreover, in employing a bottom-up early warning system within a drought response framework, it may be useful to combine and triangulate results obtained from different data sources, to enable the verification and comparison of results obtained through each source of early warning data individually.

# IV.5 Hotspots assessments

Additionally to the use of the LEAP and LIAS tools to detect droughts and their impacts, the NDRMC (former EWRD) conducts hotspots assessments on a quarterly basis. The objective of the assessments is to monitor the food security situation in Ethiopia, to be able to initiate early action where hotspots are identified. Hotspots assessments as well as other _ad hoc_ assessments can also be launched by the NDRMC to monitor a deteriorating food security situation and enable early action.

Hotspots analyses are conducted using a uniform set of food security and nutritional criteria to ensure comparability across regions. Hotspots analyses have two key objectives: (i) to prioritize the allocation of scarce financial resources, and (ii) to conduct Targeted Supplementary Feeding (TSF) in identified hotspot _woredas_ . Table 3 below details the classes of hotspots assessments and their description.

TABLE 3: HOTSPOT CLASSES AND THEIR DESCRIPTION (REPRODUCED FROM FDRE 2014B).

||**Area**<br>**of**<br>**concern**<br>**/**<br>**hotspot**<br>**level**<br>**classification**|**Class description**|**General IPC Equivalent**|
|---|---|---|---|
|Priority 1|Very severe|Hazards of high damaging level have occurred and<br>affected the lives and livelihoods of the population<br>with very severe lack of adequate food security and<br>may include excess mortality, very high and<br>increasing malnutrition, and irreversible livelihood<br>depletion.|Humanitarian Emergency|
|Priority 2|Severe|Hazards of high damaging level have occurred and<br>affected the lives and livelihoods of thepopulation|Acute<br>Food<br>and<br>Livelihood Crisis|

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|||with high stress and lack of adequate food security<br>which resulted in high level of malnutrition and<br>accelerated depletion of livelihood assets.||
|---|---|---|---|
|Priority 3|Moderate|Hazards have occurred and affected the lives and<br>livelihoods of the population moderately so that<br>most households are at risk to adequate food<br>securityin a stable manner.|Moderate Food Insecure<br>or Chronically Food<br>Insecure|

In particular, as part of the Continuum of Response, hotspots assessments are used to allocate resources when the PSNP’s federal contingency budget and HRD resources are insufficient to meet the needs of the food insecure population. If this case arises, hotspots analyses are used to prioritize _woredas_ that are in particularly critical need of food assistance. Moreover, hotspots assessments are used to identify _woredas_ to be included in TSF, with TSF usually being conducted in priority 1 _woredas_ . TSF may also be conducted as a results of (i) routine screening in Amhara, Oromiya, SNNP and Tigray and in Afar and Somali where the Health Extension Programme (HEP) is functional; (ii) six-monthly screening in Afar and Somali where HEP is not functional and; (iii) ad-hoc screening, in between the six-monthly screening, in Afar and Somali if the food security deteriorates. Screening is managed by the Woreda Health Office and implemented by health extension workers, and identifies children who are moderately or severely acutely malnourished<sup>51</sup> .

# IV.6 Integrated Food Security Phase Classification

A further tool to monitor food security consists in the Integrated Food Security Phase Classification (IPC). The IPC consists of a set of standardized tools that integrate food security, nutrition and livelihood information to classify the severity and magnitude of food insecurity, in order to facilitate early action to respond to drought. Thereby, the IPC makes use of an evidence-based approach based on international standards, to measure food security on a common scale that allows for comparability of situations across countries and over time. The IPC makes a distinction between chronic and acute food insecurity, where initial tools have been developed to measure and assess chronic food insecurity. To measure acute food insecurity, the IPC provides a classification according to five phases as detailed in Table 4 below. Thereby, the severity of the food security situation is assessed both for the time the analysis is conducted, and for a future point in time, to enable proactive decision-making as the food security situation evolves.

|TABLE4: THEIPC 2.0 FOODSECURITYSCALE|
|---|

|**Phase**|**Description**|
|---|---|
|None / Minimal|More than four in five households (HHs) are able to meet essential food and non- food<br>needs without engaging in atypical, unsustainable strategies to access food and<br>income,includinganyreliance on humanitarian assistance.|
|Stressed|Even with any humanitarian assistance at least one in five HHs in the area have the<br>following or worse: Minimally adequate food consumption but are unable to afford<br>some essential non-food expenditures without engaging in irreversible coping<br>strategies.|
|Crisis|Even with any humanitarian assistance at least one in five HHs in the area have the<br>following or worse: Food consumption gaps with high or above usual acute<br>malnutrition,OR Are marginallyable to meet minimum food needs onlywith|

51 FDRE (2014a).

18
||accelerated depletion of livelihood assets that will lead to food consumption gaps.|
|---|---|
|Emergency|Even with any humanitarian assistance at least one in five HHs in the area have the<br>following or worse: Large food consumption gaps resulting in very high acute<br>malnutrition and excess mortality, OR Extreme loss of livelihood assets that will lead to<br>food consumptiongaps in the short term.|
|Famine|Even with any humanitarian assistance at least one in five HHs in the area have an<br>extreme lack of food and other basic needs where starvation, death, and destitution<br>are evident. (Evidence for all three criteria of food consumption, wasting, and CDR is<br>required to classifyFamine.)|

In Ethiopia, the IPC 2.0 scale is used by the Famine Early Warning Systems Network (FEWS NET) in the development of food security maps. FEWS NET food security maps additionally provide information on the location of the provision of humanitarian assistance. Within Ethiopia’s broader early warning framework, the IPC system could be used to substantiate the results obtained through the existing early warning tools. Specifically, the IPC could be used to act as an overlay that could structure the decision making process to respond to droughts, particularly due to the system’s design that enables the simplification of complex information into actionable knowledge and response objectives.

# IV.7 Potential for use of early warning tools to inform early action

As outlined above, Ethiopia has a set of instruments to predict droughts, thereby enabling early action before the food security impacts of the drought become critical, that is, following the lean season (or dry season in pastoral areas) after the harvest. Based on the available early warning systems, it is possible to define triggering events that would set off a drought response. Potential triggers that could be considered include, for instance, LEAP’s intermediary outputs (WRSI or crop yield reduction estimates), or estimated beneficiary numbers based on either the LEAP or LIAS tools, or a combination thereof. In particular, by combining data obtained from the different early warning systems, it is possible to triangulate data sources to obtain a more accurate estimate of drought-related food insecurity. Based on pre-defined, objective triggers, the timeliness of drought response through Ethiopia’s PSNP could be enhanced, as detailed in section V below.

The accuracy of early warning information is dependent on the quantity and quality of input data, and the robustness of the tool’s methodology in predicting a disaster. The LEAP and LIAS tools are already based on a substantive amount of information<sup>52</sup> . However, the predictive accuracy of the tools will be enhanced further with time, as more and better data becomes available and the tools are being refined. With the development of the LEAP and LIAS tools, the GoE, WFP and wider donor community within Ethiopia have made significant advances in developing the necessary instruments to facilitate early action. The processes of further extending the LEAP tool and re-initiating the use of the HEA methodology as part of seasonal needs assessments would further enhance Ethiopia’s early warning framework, thereby facilitating early action to protect lives and livelihoods from the adverse impacts of droughts.

> 52 For instance, the LEAP tool makes use of different pre-loaded rainfall datasets, including the Rain Fall Estimate (RFE) datasets RFE 1 and RFE 2 produced by the United States Climate Prediction Center, ARC2 and TAMSAT data, as well as data produced by the NMA (Ethiopia’s National Meteorological Agency).

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# V. Timing of early warning and drought response

## V.1 Timeline of drought impacts and early warning information

In responding to drought events, the timeliness of the response is key in preventing harmful welfare impacts of the drought. The existing early warning tools in Ethiopia can be used to facilitate early action during the onset of a drought, thereby preventing the adverse welfare impacts of droughts.

Early action could be based on variables obtained from the existing early warning systems. For instance, as detailed in section IV.1.1, the LEAP tool produces the intermediate outputs of planting dates, WRSI and crop loss, as well as the final output of beneficiary numbers. Intermediary outputs are available for every dekad (10 day interval) as rainfall data is collected on a continual basis. While outputs can be produced by the LEAP tool at any point in time, the accuracy of estimates increases as the season progresses and the rainfall pattern during the season emerges. If intermediate outputs – such as crop loss – or the final output of beneficiary estimates are computed early during any given season, the LEAP tool assumes a normal rainfall pattern for the remainder of the season, leading to less accurate estimations. LEAP output variables are most accurate when computed in December for the _meher_ season, and in July for the _belg_ season. However, it would be possible to use the LEAP tool to predict drought as early as August / September in any given year, thereby enabling a timely drought response as early as December<sup>53</sup> . This could make a critical difference in protecting lives and livelihoods, as detailed in Section V.2 below.

Currently, the LEAP early warning information is produced jointly by GoE and the WFP, based on available rainfall data. LEAP outputs are developed and monitored within the NDRMC (former DRMFSS) as part of the GoE’s overall drought monitoring and assessment framework. While LEAP data constitutes a key component of Ethiopia’s early warning framework, at present, the data is not made available publicly.

As detailed in section IV.1.2, LIAS data have been collected on a bi-annual basis as part of the seasonal needs assessment process. LIAS data are collected during the months of November for the _meher_ season, and in June for the _belg_ season, and have been collected and held within the LIU at DRMFSS. Additionally to DRMFSS, LIAS data have been held within DP institutions, NGOs and private sector companies.

> 53 At present, LEAP can produce WRSI extended to the end of the season based on climatology. A further enhancement of the tool could consist in the integration of probabilities of end of season results based on seasonal forecasts. By extending the LEAP tool in this manner, it would be possible to show higher risks of a poor end of the season, and hence a greater need to act earlier.

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<!-- Start of picture text --> JUL AUG SEP OcT NOV DEC JAN FEB MAR APR MAY JUN JUL<br>: : i 1<br>apcu || tH 1H 1H |} {H 1{ 1H<br>—-5 | | \ H LIAS meher assessment } i Hl i<br>22iiiHHHH<br>sE LEAP belg beneficiary numbers : LEAP meher beneficiary numbers H H H H<br>n T T Hi<br>Revised HRD released H | H H i i<br>SS a ec<br>8 JUL AUG SEP OCT NOV DEC JAN FEB MAR APR MAY JUN JUL<br>€ G H { H H H H i t i<br>of | ' H H H ’<br>BS Planting H i Meher harvest | H ' Planting<br>P=] H H H H H<br>=7.)© Kiremt- rains. 1iH 1HH t!: HHH 'Hi 1iiai :<br>Lean H H ' H H H ean)<br>i- season - i1 H'1 'Hit H1 H1 H1 oom)<br>& JUL AUG SEP OcT NOV DEC JAN FEB MAR APR MAY JUN JUL<br>vo ~ - ¥<br>go Belg harvest : q Meher harvest H H | H i Belg harvest<br>ag2 Kiremt rains ii\ H'\ Belg rains (deat:<br>do H i rains<br>2) H H H i i<br>Meher Plantinga | 1H { Belg PlantingA 1 H 1 H Meher<br>ri{'"<br>H‘ H ‘ H Lean season<br>H : : : '<br><!-- End of picture text -->
<!-- Start of picture text --> Numbers of Harvest cycle Livelihood<br>HRD Appeals<br>months post Farmers actions (average farmer) protection Early Warning Data Process and<br>harvest threshold and and Response<br>Response<br>survival threshold<br>-2 Look for non-farm work<br>Rainfall fails<br>-1 Eat less preferred food<br>0 Harvest what is there<br>LIAS data available<br>Use savings, sell non-productive assets<br>1 Harvesting Borrow money from those not affected LEAP data available<br>Cut back on durable purchases<br>2 Two-season: planting for Cut back on input investments<br>HRD released<br>next season (if two cropping seasons)<br>3<br>Reduce food intake<br>4<br>5<br>Respond to save<br>6 One season: planting for livelihoods<br>next season<br>7 Sell productive assets Possible Response<br>through PSNP FCB<br>8<br>9 Possible Response<br>through<br>10 Respond to save humanitarian<br>Increased mortality<br>lives assistance<br>11<br><!-- End of picture text --> FIGURE 5: A STYLIZED TIMELINE OF DROUGHT, FARMER COPING STRATEGIES AND RESPONSE VIA PSNP FCB AND HRD APPEALS (ADAPTED FROM CLARKE AND HILL 2012) Typically, if crops are lost due to the failure of rains, during the first two months following the harvesting season households employ relatively low cost coping strategies. These may include changing the composition of their food intake, looking for alternative employment opportunities, selling non-essential assets or borrowing money. When the use of these initial coping strategies is insufficient to ensure adequate food security, households may resort to higher cost coping strategies, such as reducing food intake and selling productive assets<sup>58</sup> , or, for farmers in _belg_ -receiving areas, investing less in farming inputs for the next season. These coping strategies may have long-term impacts on the household’s livelihood strategy, and would be used by households between two and eight months following the main harvesting season as grain stocks decline and foods needs cannot be met. Upon the exhaustion of all high-cost coping strategies, drought-related mortality increases; this would occur approximately 10 or 11 months following the main harvest. Household consumption at this point would fall below the survival threshold and live-saving interventions become necessary.

Timely interventions to address food insecurity resulting from drought can prevent the need of farmers to employ high-cost coping strategies, thereby protecting lives and livelihoods. On the assumption that interventions through the PSNP’s FCB would be possible within two months of the release of early warning information (Hobson and Campbell, 2012), a drought response would be feasible during the

> 58 As Clarke and Hill (2013) discuss, based on evidence of the use of coping strategies, it is not clear whether households typically employ the coping strategy of reducing food intake or selling productive assets first. However, Lybbert et al. (2004) shows that drought in Southern Ethiopia did not cause farmers to sell livestock, indicating that typically farmers try to avert the loss of productive assets and use this coping strategy only when all other coping strategies are insufficient to secure food requirements.

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early stages of the use of harmful coping strategies by farmers (three to eight months following the main harvesting season), and prior to the phase where live-saving interventions become necessary (nine to eleven months following the main harvesting season). Moreover, seasonal monitoring data are available earlier than LEAP and LIAS data, enabling the detection of a failed start of a season, as well as rainfall deficits prior to the final phase of crop development. Jointly, monitoring and early warning data could therefore enable a timely drought response<sup>59</sup> . In contrast, interventions through HRD appeals are typically associated with time delays as funds are raised, leading to response interventions during the time where coping strategies are near their exhaustion and emergency assistance is required (eight to eleven months following the main harvesting season). The timing of HRD appeal funding makes the HRD process particularly well-suited to addressing emergency needs that may arise when PSNP FCB funds are exhausted early.

# V.2 The welfare benefits of a timely drought response

The impacts of droughts on household welfare have been studied in the academic literature. In particular, a body of research in Ethiopia has demonstrated that droughts have significant adverse impacts on household consumption and may cause households to lose valuable productive assets. Droughts also negatively affect farmers’ health, leading to a higher incidence of morbidity caused by the development of a range of diseases.

Investigating the impacts of droughts on household consumption, Dercon, Hoddinott and Woldehanna (2005) show that experiencing a drought at least once in the previous five years lowers per capita consumption by 20%. Moreover, the authors show that 41% of households lose productive assets and 77% of households experience a loss of income and consumption as a result of droughts. In a similar spirit, Yamano, Alderman and Christiaensen (2005) showed that crop losses associated with droughts result in reduced consumption, affecting the growth of Ethiopian children particularly in the 6-24 months age group. Estimates suggest a 50% crop loss results in a reduction of 9 mm of height over six months. A recent study by Devarajan et al. (2013) shows that a severe drought in Ethiopia would cause an immediate reduction in consumption of 6 percentage points. The authors show also that droughts may lead to significant increases in food prices, which may further jeopardize farmers’ ability to secure their food needs.

A late response to droughts will imply a greater need for providing food assistance. This link has been studied by Alemu et al. (2008), who find that in Ethiopia, grain storage will decline following the main _meher_ harvesting season. In particular, on average grain storage of the median farmer in Ethiopia will last up to seven months following the _meher_ harvest, with a decreasing proportion of all farming households having grain in storage as the season progresses (Clarke and Hill, 2012). As illustrated in Figure 6, the duration of the stocks depends on farm size, with just 35% of the stocks of small farmers lasting six months, while more than 60% of the stocks of large farms did. After ten or eleven months following the _meher_ harvest, the proportion of farms that hold grain storage is low for all farm sizes.

> 59 A timely drought response is key not only due to food insecurity, but also due to non-food needs. In particular, in a severe drought, water, and health become more important as the food security outcomes tend to be more severe. These sectors often have less capacity and are more costly per person.  Timely intervention is therefore key to enable a comprehensive drought response in both the food and non-food sectors.

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<!-- Start of picture text --> 100<br>——Smaill farms<br>& 90<br>§ —— Medium farms<br>& 80<br>& Large farms<br>§ 70.<br>c<br>F3 —Total<br>. 60 -—— ———<br>=a so _—— ——<br>fo}<br>£<br>= 40 —__—.<br>E<br>£ 30 =<br>S<br>3 20<br>§<br>¢<br>& 10><br>0<br>o 1 #2 3 4 5 6 7 8 9 0 th WR<br>Months after Meher harvest<br><!-- End of picture text -->
and facilitated the development of information to manage drought risks. Based on these significant advances, Ethiopia is well-placed to further reduce its vulnerability to drought risks.

A key feature in sound and sustainable drought risks management is the ability to respond early. In particular, as this paper showed, the large adverse impacts of droughts are compounded when assistance is provided late. A late response to droughts is associated with reductions in consumption, long-term welfare losses, malnutrition and excess mortality. By using early warning systems to trigger early action, it is possible to reduce the negative impacts of droughts on household welfare, and to protect Ethiopian farmers from the loss of lives and livelihoods.

With the LEAP and LIAS tools, combined with hotspots assessments, Ethiopia has the building blocks for a sound framework for early warning. Based on the LEAP and LIAS tools, it is possible to define early noregrets triggers for the release of the PSNP’s FCB. In particular, the LEAP and LIAS tools produce a range of variables, such as WRSI, yield reduction, and beneficiary numbers that could be used to define early no-regrets triggers. Basing actions during the onset of a drought on clearly defined triggers could lead to an increase in the timeliness of drought response, and would enhance the objectivity and transparency of the process to respond to droughts. For instance, the LEAP tool could be used to produce early warning data as early as August / September during a given calendar year, enabling a drought response by December, thereby protecting lives and livelihoods from the adverse impacts of droughts. The integration of seasonal climate forecasts into LEAP will provide a stronger basis for applying earlier crop production and needs estimates from LEAP.

The GoE and its DPs are continually improving the existing warning tools to address current limitations. For instance, the GoE, ACCRA and Oxfam are jointly developing a tool to estimate need based on _woreda_ risk profiles. Moreover, the NDRMC is working on the development of a multi-sector early warning system based on _woreda_ risk profiles, thereby further enhancing its existing early warning framework. In addition, the existing LEAP tool is being refined to include market data, to incorporate information to predict need in the pastoral regions of Afar, Somali and Borena, and to predict needs associated with flood risks. Similarly, LIAS baselines for the highlands regions are being updated in the course of 2016, to reinitiate the collection of LIAS data; with updated baselines and continued investment in the tool, it would be possible to reinitiate the use of LIAS data as part of seasonal needs assessments. These extensions and improvements will further strengthen Ethiopia’s early warning systems. Moreover, as more and better data become available with time, the predictive accuracy of the LEAP and LIAS tools will be improved further. The LEAP and LIAS tools in combination with hotspots and IPC assessments could be used jointly to trigger early action, where different sources of data could be triangulated to enhance accuracy.

Ethiopia’s current drought response mechanisms are centered on the use of PSNP contingency budgets and the HRD appeals process. The integration between the PSNP’s FCB and HRD Appeal process could be enhanced further by combining fast, early action based on early warning data with a slower response through the HRD process. In particular, approximate early warning-based no-regrets triggers could be used as a mechanism to effectively address needs associated with high-frequency, low-intensity drought events. Early warning data collected during the beginning of a rainy season (for instance, in August / September for the _meher_ season and in April / May for the _belg_ season) could be used to trigger early action, thereby preventing some of the worst impacts of a drought. By contrast, the HRD process could 25
be used to respond to residual needs associated with low-frequency, high-intensity events. This could enable a cost-effective and timely drought response.

Key advantages of this approach would include that implementing a risk layering approach could allow Ethiopia to match the timing of the availability of resources more closely with need. Moreover, with sufficient financing for the PSNP’s FCB, humanitarian appeals could be used to address high intensity events only, allowing Ethiopia to reduce its recourse to humanitarian resources in addressing drought risks. Finally, by combining risk financing instruments as part of a risk layering approach, Ethiopia could enhance the predictability of funding, such that the timing of funding would coincide, to a greater extent, with need. The implementation of a risk layering strategy could therefore also limit the negative welfare impacts associated with a late response to drought.

As this paper has argued, droughts cause a toll on development, dampen growth and divert scarce resources from development efforts. The systematic management of drought risks is therefore an important factor in enabling Ethiopia to sustain its impressive growth trajectory over the past ten years, and facilitating Ethiopia’s further progress towards its goal of becoming a middle-income country by 2025. The objective of further enhancing its drought management capacity is expressed, for instance, in the DRM SPIF: _“As aggressive goals of broad based economic growth and social development are envisioned for the country in the coming five years, a comprehensive Disaster Risk Management (DRM) system is called for to reduce disaster risk and the impacts of disasters, and to protect development gains.”_ (FDRE 2014d) The DRM-SPIF also reiterates the importance of early warning information in enabling a sound DRM framework: “ _The GTP emphasizes the importance of strengthening Ethiopia’s existing early warning system and the capacity to respond to disasters_ ” (FDRE 2014 d).

Leveraging the scope of existing early warning tools to inform early action, further improving the early warning framework and enhancing the role of early warning tools within the Continuum of Response would allow Ethiopia to progress further in achieving these objectives, thereby protecting its development gains and ultimately safeguarding the lives and livelihoods of Ethiopians vulnerable to drought risks.

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