3.a Gender and Livelihoods_Cropping_draft December 20 (1) (2).pdf

HOUSEHOLD ECONOMY ANALYSIS

UTILIZATION ACTIVITY

GENDER AND LIVELIHOODS

Disclaimer

The authors’ views expressed in this report do not necessarily reflect the views of the United States Agency for

International Development(USAID) or the United States Government.

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CROPPING CLUSTER

LIVELIHOOD ZONES

ETHIOPIA

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Acknowledgements

This report was prepared by the Household Economy Approach Utilization Activity (HEA U Activity) funded by the United States Agency for International Development (USAID). The HEA U Activity is managed by Save the Children Federation (SC) and implemented in partnership with the Government of Ethiopia National Disaster Risk Management Commission (NDRMC). In-country leadership for the HEA U Activity is provided by the Chief of Party, Waddington Chinogwenya, and Kinfe Terefe, Senior Livelihoods and Disaster Risk Management Advisor.

This report on gender and livelihoods was written by Jennifer Bush of the Food Economy Group (FEG

Consulting). The HEA U Activity greatly appreciates the close collaboration with the NDRMC Early

Warning and Response Directorate who led efforts to mobilize and coordinate Federal and Regional

Government of Ethiopia and humanitarian partners to gather and analyze the HEA baseline information on which this report is based.

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Table of Contents

Executive Summary

Background

Objectives

Methodology

The Cropping Cluster

The Feminization of Poverty

The Poor Wealth Group - FHH and MHH

Assets

Sources of Food

Sources of Cash Income

Expenditure Patterns

Livelihood Impacts

The Deficit Food Crops Cluster

The Food Crops Cluster

The Food Crops & Livestock Cluster

The Cash Crops Cluster

Conclusions and Recommendations

Conclusions

Recommendations

Annex I – Data tables

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Executive Summary

The central question of the HEA Gender and Livelihoods study is whether poor female headed households (FHH), are a separate household type from poor MHH? Do poor FHH produce and earn as much as poor MHH? Are poor

FHH vulnerable to different shocks than poor MHH and/or at greater risk of food insecurity? Given prevailing patterns of livelihood, does the same shock have different outcomes on poor FHH compared to poor MHH?

The study answers these questions in two parts. The first part is a comparative analysis of the livelihoods of poor

FHH and poor MHH to highlight differences and similarities in their livelihood strategies. Are poor FHH typically

“poorer” with fewer assets compared to poor MHH? Do food and income sources vary depending on the household head and the type of work women can access? Do poor FHH rely on income options that are low value and/or that generate less income than poor MHH? This first section also examines HEA wealth group data to see if

FHH are concentrated in the poorest categories or are evenly spread across wealth groups.

The second part of the study examines the impact of a bad year on food and income and looks to section 1 to understand causes of any marked differences in outcomes. The objective of the analysis is to understand whether, and to what extent, poor FHH are at greater risk of food insecurity in bad years compared to poor MHH. How is food and income access affected by a recent shock and how does this differ by wealth group? The situation of middle households is brought into the analysis in this second part of the study to see what level of assets provides a safety net for a household in bad years as well as a route to food security and resilience in good years.

Findings and Conclusions

1. On average across all the regions in the cropping mega cluster, FHH comprise 40% and 30% of very poor and poor households. This data that points to the feminization of poverty.

2. Within the poor wealth group, poor FHH form a sub-group who are that little bit poorer than poor MHH. Overall, poor FHH earned on average ETB 3,131, ETB 2,566, and ETB 1,713 less per year than poor MHH in the surplus food crops, cash crops and deficit food crops clusters respectively. In total income terms, poor FHH also earned less than poor MHH.

3. In terms of assets, poor FHH on average own and cultivate less land than poor MHH (about 0.1ha) and own about 0.5 head less than poor MHH for each of the main types of livestock. Their households are smaller and poor FHH typically lack 0.5-1 adult male.

4. In the bad year scenario, results from 2019 seasonal assessments in sample zones showed that the income gap between middle and poor households, and between poor MHH and poor FHH, narrowed but poor FHH in general remained worse off as their reference year total income was lower. The amount of the remaining gap between poor MHH and poor FHH ranged from ETB

500/HH/year to ETB 2,000/HH/year in the sample zones.

Recommendations

1. Recognize the needs of poor FHH through gender disaggregated seasonal assessments using

LIASs set up to include FHH.

2. Acknowledge that poor FHH often find it more difficult to escape poverty and thus may be on

PSNP caseloads longer than poor MHH.

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Background

Objectives

The HEA Gender and Livelihoods study makes use of the livelihood information on poor female headed households that was collected at the village level across Ethiopia under the USAID-funded Household Economy

Approach Utilization Activity (HEA-U). The livelihood information draws from a database that covers 181 livelihood zones1. These livelihood zones can be grouped into larger livelihood clusters, including pastoral, agropastoral, mixed food crops and livestock, food crops and, lastly, a cash crop cluster. PART A of this broad study focused on the pastoral and agropastoral cluster which includes a total of 36 livelihood zones of which 22 were included in the study. PART B – this current report -- focuses on the cropping mega cluster, including 3 main sub-clusters that cover 4 regions and 132 livelihood zones in total of which 50 are in Oromia Region, 23 are in Amhara Region, 16 are in Tigray Region, and 42 are in SNNPR.

The objective of the study is to examine in detail the HEA data on poor FHH and poor MHH across the 132 different livelihood zones. This new study will complement available HEA resources and HEA analyses, including individual livelihood profiles for each zone, regional overviews and the HEA change analyses. These documents already contain some level of description and analysis of the situation of poor FHH, but this study goes further by both providing a broader perspective at the cluster level and (conversely) focusing more exclusively on poor households

(both poor MHH and poor FHH) using a comparative analysis lens to assess differences between the sub-groups.

The central question of this study on livelihoods and gender is whether poor female headed households (FHH) are particularly disadvantaged as a household type. Do poor FHH produce and earn as much as poor MHH? In face of recent shocks, what has been the impact on their livelihoods and given their pattern of livelihood, are poor FHH particularly vulnerable to typical shocks?

This study answers these questions in two parts. The first part is a comparative analysis of the livelihood strategies of poor FHH and poor MHH to highlight differences and similarities in livelihood options. Are poor FHH typically “poorer” with fewer assets compared to poor MHH? Do food and income sources vary depending on the household head and what type of work women can access? Do poor FHH rely on income options that are low value and/or that generate less income than poor MHH? This first section also examines HEA wealth group data to see if there is a “femininization of poverty”. Are female-headed households proportionately concentrated in the poor wealth groups?

The second part of the study examines the impact on food and income access of a bad year and looks to section 1 to understand causes of any marked differences in outcomes. The objective of the analysis is to understand whether, and to what extent, poor FHH are at greater risk of food insecurity in bad years compared to poor MHH.

How were food and income access affected by a recent shock and how does this differ by wealth group? The situation of middle households is brought into the analysis in this second part to see what level of assets provides a safety net for households in bad years as well as a route to food security and resilience in good years.

Methodology

This study relies on field work undertaken by the HEA Project to update livelihood baseline data in 181 livelihood zones across Ethiopia. HEA involves the collection of raw data at the woreda, village and household level by local field teams under the technical guidance of a trained HEA team leader. Village data on production patterns and typical yields by type of crop and livestock are triangulated during interviews with household-level data that

1 The livelihood zones covered by the HEA Project are located in Oromia, Amhara, SNNPR, Sidama, Afar and Somali regions.

Benashangul Gumuz aand Gambella regions are not included in the analyses.

6 | P a g e involves a detailed quantification of household assets, food and income sources, patterns of expenditure and bad year coping strategies. All data is disaggregated by wealth group and references a specific year (called the reference year). HEA baseline data is integrated into a framework of analysis that can both measure the impact of actual shocks or model forecasted price and production changes on household food and income. Two thresholds are used to measure needs: the survival threshold (below which households face hunger) and the livelihood protection threshold (below which households face livelihood erosion). Reference material describing the HEA methodology include the HEA Practitioner’s Handbook (see FEG’s website (http://foodeconomy.com/resources/).

No new field work was carried out for this study. Instead, the study relies exclusively on the new HEA baselines from the 2013-2016 reference years. This is possible because the new baselines included a fifth wealth group – poor FHH. The data on poor FHH provides a rich source of information for comparative analysis. The table below lists all the individual baselines that form the cropping sub-cluster: (i) surplus food crops; (ii) deficit food crops; and

(iii) cash crops. Oromia Region’s crops & livestock zones are included as a separate sub-cluster where relevant.

Region/Cluster LZ code LZ name LZ #

AMHARA GHL Guna Highland Barley and Potato AMH4

Food Secure Highlands MTS Minjar Teff and Sorghum AMH7

NSW North Shewa Highland Wheat and Teff Productive AMH11

SWM Southwest Maize, Finger Millet and Teff Productive AMH18

SWT Southwest Woina Dega Teff AMH20

SWW Southwest Woina Dega Wheat AMH21

Food Secure Lowlands CHV Cheffa Valley AMH3

MLS Merhabete Lowland Sorghum & Teff AMH5

MTL Minjar Lowland Sorghum and Teff AMH6

NWC Northwest Cash Crop AMH13

NWE North Wollo East Plain AMH14

NWS Northwest Sorghum Belt AMH15

Borderline Food Secure CBP Central Highland Barley and Potato AMH2

NSS North Shewa Highland Sheep and Barley AMH10

NWB Northern Highland Wheat, Barley and Sheep AMH12

SWS South Wollo & Oromia Lowland Sorghum and Cattle AMH19

Food Insecure Highlands NHB North Wollo Highland Belg AMH8

NMC North East Woina Dega Mixed Cereal AMH9

SME South Wollo Meher AMH16

SWB South Wollo Belg AMH17

WMB South Wollo Meher and Belg AMH23

Food Insecure Lowlands ATW Abay Tekeze Watershed AMH1

& Catchment Areas TSG Tekeze Lowland Sorghum and Goats AMH22

Region/Cluster LZ code LZ name LZ #

TIGRAY ALL Adiabo Lowland TG1

Surplus producing lowlands HSS Humera Sorghum and Sesame TG8

RVL Raya Valley TG12

WSE Western Cereal and Sesame TG16

Deficit woina-dega WCT Western Central Teff TG14

CMC Central Mixed Crop TG4

EPL Eastern Plateau TG6

EDM Enderta Dry Midland TG5

Deficit highland AOH Alaje Ofla Highland TG2

AWH Atsbi Wonberta Highland TG3

GWH Gesho and Wheat TG7

Deficit catchment areas TSC Tsirare Catchment TG13

MTK Middle Tekeze TG11

WRC Werie Catchment TG15

MRB Mereb Basin TG10

IRM Irob Mountain TG9

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OROMIA CGC Chercher & Golocha Chole Coffee, Maize and Chat ORM16

CASH CROPS CVG Chat and Vegetables ORM19

Northeastern Cash Crops SMC Sorghum, Maize, & Chat ORM55

WBP Wheat, Barley & Potato ORM57

Central-eastern Cash & AFC Agarfa, Gasera, Ginir and Golocha Fruit, Coffee and Chat ORM5

Niche Crops BGE Borena-Guji Coffee and Enset ORM12

DBC Dallo, Barbare and Arana-Buluk Coffee ORM20

GSW Goro-Ginir Spice and Wheat ORM30

STS Shirka Ticho Spice and Teff ORM56

Western Coffee & Forest ACH Anfilo Coffee Monoculture & Honey ORM2

DLS Dale-Lalo Sorghum & Maize ORM21

GHT Gera-Setema-Sale Forest Teff, Honey & Cattle ORM28

IMP Illu-Wellega-Birbir Maize, Peppers & Sesame ORM32

JCC Jimma-Illubabur Coffee, Cereals & Chat ORM33

NMT Nadda-Gilgel Gibe Maize, Teff & Sorghum ORM45

QBC Keto-Begi Cereals & Cattle ORM49

WMS Wellega Coffee, Maize & Sorghum ORM60

FOOD CROPS ABW Arsi Bale Wheat, Barley & Potato ORM1

Eastern Cereals ADF Adaba Dodola Community Forest ORM4

GBC Guji-Borena Enset, Barley & Cattle ORM25

RCS Robe Chole Sude and Seru Teff, Maize, Haricot Bean Belt ORM50

RVM Rift Valley Maize and Horse Bean ORM51

SKV Siraro Kofele Potato and Vegetables ORM54

Central Highland Crops AMT Ambo Selale Ginderberet Teff & Wheat ORM6

AWT Ameya Woliso-Ambo Teff & Cattle ORM9

BAT Becho-Adea Teff & Chickpea ORM10

CIE Chebo-Inchini Enset, Barley & Cattle ORM18

GBE Gerbo Enset & Chat ORM26

GMP Gibe Maize & Peppers ORM29

JDB Jaldu-Dendi-Ifata Barley & Potato ORM34

MAW Melka Awash-Ochocha mid-highland Wheat & Teff ORM38

SAW Selale-Ambo Highland Barley, Wheat & Horsebean Belt ORM52

Western Cereals ADC Abe Dongoro-Diga-Sasiga Coffee & Sorghum ORM3

ASM Abay Gorge Sesame, Maize & Honey ORM8

CHT Central Horo Teff, Nuug & Cattle ORM17

DSM Didessa-Gibe-Warma Valley Sorghum, Maize & Oil crops ORM22

NTF Nego-Dilla Teff, Finger Millet & Nuug ORM47

FOOD CROPS & LIVESTOCK BGC Borena-Guji Maize, Wheat & Livestock ORM11

Eastern Food Crops & L/stock GBG Gursum-Babile Groundnuts & Sorghum ORM27

KKE Kofele Kokosa Enset & Cattle Belt ORM36

Central Food Crops & L/stock MAS Muger-Abay-Jema Sorghum & Teff Belt ORM37

SHE Soddo Highland Wheat, Barley & Enset ORM53

WKL Wuchale-Abichu-Kembebit L/stock, Wild Oats & Barley/Wheat ORM59

Western Food Crops & L/stock ETC Ebantu-Limu Teff & Cattle ORM23

GAM Guduru-Amuru Maize, Teff & Cattle ORM24

HSF Anger Maize, Sorghum & Finger Millet ORM31

JTM Jimma-Yama Logi Teff & Maize ORM35

MMC Mendi-Dabisu Maize, Sesame & Cattle ORM43

NTC Nole-Meko Diga Teff & Cattle ORM46

WDH Wellega-Dedo Highland Wheat & Barley ORM58

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This analysis of poor FHH livelihoods is not meant as an exhaustive study of gender and livelihoods in Ethiopia and as such a literature review was not conducted. Instead, the goal is to examine in more depth the HEA data on poor

FHH and to provide a comparative perspective at the cluster level to see if this closer look at the situation of poor

FHH brings any new understanding about gender, poverty, and livelihoods in Ethiopia.

SNNPR ACE Amaro Coffee and Enset SNP2

FOOD CROPS BAM Badewacho Alaba Maize SNP4

Food Crops (24 LZs) BCE Bench Keffa Cereal and Enset SNP5

DMR Dawro Konta Maize and Root Crop SNP8

GCE Golcha Melga Chat and Enset SNP10

GEB Gurage Siltie Highland Enset and Barley SNP12

GET Gurage Siltie Enset and Teff SNP14

GGE Gamo Gofa Enset and Barley SNP15

GLM Gurage Lowland Maize and Teff SNP16

GMR Gamo Gofa Maize and Root Crop SNP17

GUT Gumayde Teff SNP18

HMZ Hadiya Maize and Teff SNP22

HWE Hadiya Kembata Cereal and Enset - Hadiya Sub-zone SNP23

KCE Hadiya Kembata Cereal and Enset - Kembata Sub-zone SNP26

KEC Kaffa Cereal and Enset SNP27

LCE Southern Special Woredas Lowland Cereal SNP28

OVM Omo Valley Maize and Sorghum SNP30

SBW South Ari Highland Barley and Wheat SNP31

SCE Southern Special Woredas Lowland Cereal SNP32

SEB Sidama Gedeo Highland Enset and Barley SNP35

SMB Sidama Maize Belt SNP36

SOC South Omo Cropping SNP38

WMR Wolayita Maize and Root Crop SNP45

WWB Wolayita Barley and Wheat SNP46

YCE Yem Cereal and Enset SNP47

CASH CROPS ABM Alaba Badawacho Maize and Pepper SNP1

Cash Crops (18 LZs) AMP Alaba Mareka Lowland Pepper SNP3

BCR Basketo Melo Coffee & Root Crop SNP6

BME Bona Enset and Maize SNP7

ECS Eastern sub-zone - Western Coffee and Spices SNP9

GCO Gedeo Coffee SNP11

GEC Gurage Siltie Highland Enset and Chat SNP13

HGM Hawella Wondo Genet Chat and Maize SNP19

HGZ Hadera Ginger SNP20

HMC Humbo Lowland Maize and Cotton SNP21

IBA Chamo Abaya Irrigated Banana SNP24

KBC Kedido Badewacho Coffee SNP25

ORF Omo River Flood Recession SNP29

SCO Sidama Coffee SNP33

WCE Sheka Cereal and Enset SNP41

WCG Wolayita Ginger and Coffee SNP42

WCS Western sub-zone - Western Coffee and Spices SNP43

WFP Western Forest Products SNP44

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The Cropping Cluster

What are the main characteristics of the cropping mega cluster? What are the livelihood strategies of poor households? Do the livelihoods of poor female headed households (FHH) share similar patterns as poor male headed households (MHH) in the cropping cluster? How important is geography and wealth group as determinants of livelihood options compared to gender? This section includes a brief description of the basic characteristics of the cropping cluster including: (i) location of cropping zones in each region; and (ii) proportion of food from crops vis a vis livestock in the cropping zones. All graphs in this section come from the Regional

Overview and Change Analyses (USAID/HEA-U 2018).

The cropping mega cluster of livelihood zones covers most of Ethiopia with the exception of Afar and Somali

Regions. The regional maps below indicate the location of the cropping livelihood zones. As a mega-cluster, cropping zones were sub-divided into smaller clusters. In Oromia Region, there are 3 sub-clusters: (i) the food crop cluster (20 LZs); (ii) the food crops & livestock cluster (13 LZs); and (iii) the cash crop cluster (17 LZs). In Amhara

Region, there are two sub-clusters: (i) the food secure cluster (16 LZs); and (ii) the food insecure cluster (7 LZs). In

Tigray Region, there are two sub-clusters: (i) the food surplus cluster (4 LZs); and the food deficit cluster (12 LZs);

and in SNNPR, there are also two sub-clusters: (i) the food crop cluster (24 LZs) and (ii) the cash crop cluster (18

LZs). In total, 132 livelihood zones comprise the cropping mega-cluster of which 50 are in Oromia Region, 23 are in Amhara Region, 16 are in Tigray Region, and 42 are in SNNPR.

Livelihood Clusters – Oromia Region Livelihood Clusters – SNNPR

Livelihood Clusters – Amhara Region Livelihood Clusters – Tigray Region

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Oromia Region – Annual Cash Income for 4 livelihood clusters

The cropping livelihood zones are characterised by the primary importance of crop production as both a food source and an income source. Taking the example of Oromia Region, the pie charts at left highlight the central role of crops in generating income in the cropping zones. For instance, crop earnings comprised 66% and 71% of annual cash income in the food crop and cash crop clusters respectively. The picture is more mixed in the food crops & livestock sub-cluster where

41% of annual cash income (weighted average across all 4 wealth groups) was derived from crop sales and 59% from livestock.

Own crop production is also a principal source of food in the cropping mega-cluster. In Amhara Region, poor households secured food principally from two sources, own-crops and purchase, a pattern that was typical both for poor MHH (at left below) and poor FHH (at right below) across the cropping livelihood zones. The similar pattern for poor FHH and poor MHH confirms that in broad terms, geography and wealth group are the main determinants of livelihood patterns, and the effect of gender on food and income access is better understood in the details (how much income earned from what sources, and so on).

Amhara Region – Poor HH Food Sources across cropping livelihood zones

The relative importance of own crops as a principal source of food differed distinctly between the food crop and cash crops cluster as seen in the example from SNNPR below. The bright green indicates the proportion of

SNNP Region – Own crops as a food source – Food Crops Cluster and Cash Crops Cluster

11 | P a g e own crops for consumption. Not surprisingly, in the food crop cluster, the contribution of own crops is relatively high. By contrast, in the cash crop cluster, own crops are an important food source, but purchase is the principal food source for the poor.

By contrast, as a weighted average by sub-cluster across all wealth groups and livelihood zones in Oromia Region, own crops contributed to more income in the cash crops cluster than in the food crops cluster. In Oromia Region, crops that brought in the most income included food crops (maize and wheat) and/or high value crops such as teff, sesame, coffee, peppers, and cumin. In SNNPR, maize, coffee and teff brought in the most income in the reference year (weighted average for all wealth groups by livelihood zones) whereas in Amhara Region, sesame, teff and eucalyptus trees were the crops that provided the most income in the highest number of livelihood zones.

Oromia Region – Own crops as an income source SNNP Region – Own crops as an income source

The cropping mega-cluster is by definition very large and hence a hallmark of this large cluster is the variety of crops grown there. On the Oromia Region map below, the principal crop for own consumption is shown by color.

Oromia Region – Principal crop for own consumption Tigray Region – Principal crops for own consumption

Maize, in bright green, is dominant in 36 of the 58 livelihood zones (62%) in Oromia Region. This is followed by wheat (12% of zones). In SNNPR, maize is also a dominant crop (in 62% of livelihood zones) but in 23% of the livelihood zones, enset is the principal food crop. By contrast, in Tigray, out of 9 typical crops, sorghum, wheat, barley/hanfets and teff provided the greatest contribution to food energy in the cropping livelihood zones in the reference year (see bar chart above, at right). Altitude plays a role too as sorghum is better suited to lowland cropping zones whereas wheat and barley do better at higher altitudes (dega and woina dega zones).

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The Feminization of Poverty

This section examines HEA wealth breakdown data and in particular the prevalence of female headed households across wealth groups. HEA findings highlight the feminization of poverty, that is that female headed households are concentrated in the poorer wealth groups. This finding alone suggests that gender is a factor in shaping the economic constraints and opportunities faced by female headed households and limit their exit from poverty. As a group, therefore, poor FHH likely face a greater risk of food insecurity and income stress than poor

MHH.

Percentage of Female Headed Households by Wealth Group and by Region

Oromia Region SNNPR

HEA baselines begin with a description of local wealth groups and a wealth breakdown including an estimate of the proportion of female headed households in each wealth group. The data from village key informants on the wealth breakdown averaged across the livelihood zones in 4 regions –Oromia, SNNPR, Amhara and Tigray– is detailed in the table below at left. Female headed households were found in all four wealth groups not just amongst the poor. Overall, very poor and poor female headed households comprised about 16% of all households across the

4 regions. By contrast, middle and better off female headed households were just 8% of all households.

A pattern that stood out was the proportion of the poor and very poor wealth groups who were female-headed. Specifically, female headed households comprised 40% and 30% of the very poor and poor wealth groups respectively. Amongst the better off, female headed households were only about 10% of the wealth group. Thus, there is a marked concentration of female headed households in the lower wealth groups. This result underscores the importance of understanding the livelihoods of poor FHH as a sub-group of the poor.

% of HH who are female-headed

Amhara Region Tigray Region

Region Wealth Group MHH FHH Total

SNNPR VP 12% 6% 18%

P 19% 8% 27%

M 31% 7% 38%

BO 15% 2% 17%

total 77% 23% 100%

Amhara VP 9% 8% 17%

P 20% 9% 29%

M 30% 7% 37%

BO 17% 2% 19%

total 75% 25% 100%

Oromia VP 13% 6% 19%

P 21% 7% 28%

M 30% 6% 36%

BO 15% 2% 17%

total 79% 21% 100%

Tigray VP 10% 9% 19%

P 18% 10% 28%

M 30% 6% 36%

BO 16% 1% 17%

total 74% 26% 100%

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The HEA wealth group data provides important evidence that female headed households are clustered in the bottom wealth groups. What is not clear is why. When women divorce or are widowed or become de facto heads of their households due to their husband’s prolonged absence, do they typically slip into poverty due to lack of secure access to assets? Or are poor women at greater risk of being widowed, divorced, or abandoned? In other words, which stressor comes first: poverty itself or losing a husband from death/divorce/abandonment? HEA baselines cannot isolate the factors that cause the feminization of poverty but HEA livelihood information -- which is specific to each wealth group including poor FHH – can help us to understand what being a poor FHH means in practice and whether they face particular challenges as a sub-group of the poor.

The Poor Wealth Group - FHH and MHH

Within the poor wealth group, how does the situation of poor female headed households compare to poor male headed households? Are most poor households constrained by the same obstacles (such as limited assets and resources, and limited education/skills) or are certain obstacles gender-specific? This section looks at the poor wealth group –disaggregated by FHH and MHH – and examines to what extent food and income patterns in the cropping mega cluster are affected by the gender of the household head.

Poor female headed households are not a single, homogenous group. Livelihood options differ by geography and by market access as the description of the cropping mega cluster showed. Bearing this in mind, what aspects of poor households’ livelihood strategies relate to the gender of the household head and what are the implications for long-term resilience? Are poor FHH typically “poorer” with fewer assets than poor MHH? Do food and income sources vary depending on the household head and what type of work women can access? Do poor FHH rely on income options that are low value and/or that generate less income than poor MHH? The objective of this section is to compare the livelihood strategies of poor FHH and poor MHH and to highlight key differences as well as similarities in the livelihoods of the poor.

Assets

On average, across the cropping mega cluster, the typical household size of poor households is about 5.5-6 people

(poor MHH) and 4.7-5.5 people (poor FHH). Households are slightly smaller in the deficit food crops sub-cluster, but the overall pattern is the same. As a rule of thumb, poor FHH have 0.5-1 fewer family members (usually an adult male). Lack of sufficient family labor can have economic implications for poor FHH affecting crop production, herd management and income generating activities. Moreover, poor FHH typically have a higher dependency ratio

(more children to adults) which also affects their labor availability (and thus earning potential) as well as the types of work that they can access and/or fit into their other domestic responsibilities.

Food crops_surplus Oromia SNNPR Amhara Tigray

POOR HH MHH FHH MHH FHH MHH FHH MHH FHH Ave MHH Ave FHH

HH size 6.0 5.1 5.8 5.2 5.5 4.6 5.5 4.5 5.7 4.8

Cash crop cluster Oromia SNNPR

POOR HH MHH FHH MHH FHH Ave MHH Ave FHH

HH size 5.9 5.1 6.0 5.4 5.9 5.3

Food crops_deficit Fdcrops + Lstock

Food crops_deficit Amhara Tigray Oromia

POOR HH MHH FHH MHH FHH Ave MHH Ave FHH MHH FHH

HH size 5.4 4.5 5.7 4.9 5.5 4.7 6.2 5.5

VP P M BO

SNNPR 33% 30% 18% 12%

Oromia 32% 25% 17% 12%

Amhara 47% 31% 18% 9%

Tigray 47% 36% 17% 6%

Average 40% 30% 18% 10%

% per WG that is FHH

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Household size and household labor are one part of the asset picture. Other key assets in the cropping mega cluster are land and livestock. In terms of farmland, across the sub-clusters and regions overall, poor FHH owned and cultivated less land than poor MHH. In the surplus food crops cluster, poor households owned and cultivated about 1ha land which was slightly higher than in the cash crops cluster (0.7-0.8ha/HH) and the deficit food crops cluster (0.6-0.7ha/HH). Across the sub-clusters, the difference in land holdings between poor FHH and poor MHH was very small, about 0.1ha/HH less for poor FHH (see graphs below). However, poor FHH cultivated slightly less of their land than poor MHH. Thus, the difference was about 0.2ha less land cultivated by poor FHH than poor

MHH. This result may reflect less labor availability as well as other factors such as fewer oxen and other inputs.

Land Assets by household type – Surplus Food Crops, Cash Crop, and Deficit Food Crops sub-clusters

Poor FHH also typically owned fewer livestock than poor MHH. During baseline assessments, data is collected on how many cattle, oxen, goats, sheep, donkeys/mules and other key livelihood assets are owned by each wealth group. Across the sub-clusters and regions, poor FHH on average owned about 0.5 head less than poor MHH for each of the main types of livestock (cattle, goats, and sheep). Oxen and donkey holdings were very low for all the poor especially in the cash crops cluster (< 0.5 ox/HH).

Poor HH Livestock Assets – Surplus Food Crops Cluster Poor HH Livestock Assets – Cash Crops Cluster

In the cropping mega cluster, poor households owned very few livestock and on average, cattle holdings were 0.5-2 cattle for poor FHH. Cattle holdings were highest in the

Oromia food crops & livestock sub-cluster (3 cows/HH) and in the surplus food crops cluster (2 cows/HH) but sheep holdings were highest in the deficit food crops cluster (2.8 sheet/HH). Differences in the main type of animal owned reflects the agroecology of the various zones in which in some areas it is feasible for the poor to have cattle and in other areas it is more feasible to mainly have sheep.

Compared to poor MHH, poor FHH owned fewer livestock, and the difference was about 0.5 head for each type of livestock. For poor FHH, smaller livestock holdings meant on average less income from livestock sales and less access to ploughing oxen. Smaller herds may also point to the challenges faced by women acquiring animals

0.0

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Ave MHH Ave FHH

Poor HH Land Assets (ha) - Cash Crop Cluster

Land area owned Land cultivated

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Ave MHH Ave FHH

Poor HH Land Assets (ha) - Food Crops_Deficit

Land area owned Land cuLtivated

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Ave MHH Ave FHH

Poor HH Land Assets (ha) - Food Crops_Surplus

Land area owned Land cultivated

0.0

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Cattle Goats Sheep Ox Donkey Beehives

Poor HH Livestock Assets - Food Crops_Surplus

Ave MHH Ave FHH

0.0

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Cattle Goats Sheep Donkeys Beehives Trees (euc.)

Poor HH Livestock Assets - Cash Crops Cluster

Ave MHH Ave FHH

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Sheep Goats CattLe Ox DonLey

Poor HH Livestock Assets - Food Crops_Deficit

Ave MHH Ave FHH

Poor HH Livestock assets _ Deficit Food Crops Cluster

15 | P a g e through herd growth, gifts and/or purchase. Poor FHH also faced labor and financial obstacles when managing the herd, including paying for inputs such as vaccinations, animal drugs, fodder, salt and so on. Beyond livestock assets, poor FHH also owned fewer beehives than poor MHH (i.e., in the surplus food crops cluster and the cash crop cluster) and, in deficit food crops cluster, fewer eucalyptus trees (13.7 trees and 11.5 trees owned by poor

MHH and poor FHH respectively). Eucalyptus trees are an important income source in some deficit cropping zones and fewer trees owned by poor FHH points again to the challenges faced by poor FHH in generating sufficient income from their assets.

Education is also a household asset because children with literacy and numeracy skills can adapt to different environments and future changes. In all three sub-clusters fewer girls were educated to Form 4 level as boys according to village key informants. In the surplus food crops cluster, the difference was almost negligible.

However, in the deficit food crops cluster, on average about 0.5 fewer girls were in school up to Form 4 both in poor FHH and poor MHH. It is not clear from the data why fewer girls than boys are educated to Form 4 levels in the deficit food crops cluster, but it possibly relates to pressures of poverty. More importantly, the findings flag a potential link between girls’ education and the feminization of poverty. Lower levels of education amongst girls repeats cycles of disadvantage affecting livelihood options for girls themselves and later for their families.

Food crops_surplus

POOR HH Ave MHH Ave FHH

Boys in school (Form 4) 1.4 1.2

Girls in school (Form 4) 1.3 1.2

Cash crops

POOR HH Ave MHH Ave FHH

Boys in school (Form 4) 1.4 1.4

Girls in school (Form 4) 1.2 1.1

Food crops_deficit

POOR HH Ave MHH Ave FHH

Boys in schooL (Form 4) 2.0 1.8

GirLs in schooL (Form 4) 1.3 1.3

16 | P a g e

Sources of Food

Poor HH Food Sources _ Surplus Food Crops Cluster by Region Poor HH Food Sources - Cash Crops Cluster by Region

Poor HH Food Sources – Deficit Food Crops Cluster

In the cropping mega-cluster of livelihood zones, poor households derive an important proportion of their food from own crops albeit with significant variation between clusters and between livelihood zones. Not surprisingly, the contribution of own crops to poor household annual food energy was lowest in the deficit food crops cluster with a range of 33% to 55% of annual food needs. The contribution of own crops was higher in the cash crop cluster – 49% to 55% of annual food needs -- and highest in the surplus food crops cluster -- 48% to 64% of annual food needs. In each cluster (averaged by region), poor

FHH secured less annual food energy from own crops than poor MHH. This result likely correlates with the finding that poor FHH had fewer key productive assets – land, labor, and livestock – than poor MHH. The differences were not substantial, but nonetheless there are implications for poor FHH, such as the need to purchase more food (and therefore needing cash to buy food) and/or consuming less food. In terms of annual food sources overall, poor

MHH secured about 100%-103% of their annual food needs and poor FHH secured a little less (98%-102%).

Poor HH crop production SNP17 - Surplus Food Crops Cluster

The example from Gamo Gofa Maize and Root Crop livelihood zone (GMR/SNP17) in SNNPR provides a fairly typical picture of production differences between poor FHH and poor MHH. In the reference year, poor households produced a similar diversity of meher season and belg season crops, including maize, sorghum, beans, cassava and sweet potatoes. However, poor FHH produced about 200kg less than poor MHH, particularly meher season sorghum and belg season maize and beans. By contrast, poor FHH produced more cassava. These production differences can be accounted for by differences in landholdings as poor FHH cultivated a little less land and may also have had less access to adult labor for farming.

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MHH FHH MHH FHH MHH FHH MHH FHH

Oromia SNNPR Amhara Tigray

Poor HH Food Sources (% annual kcals) - Food Crops_Surplus crops livestock products payment in kind purchase food aid gifts, wild food, other

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MHH FHH MHH FHH

Oromia SNNPR

Poor HH Food Sources (% annual kcals) - Cash Crop Cluster crops livestock products purchase food aid

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MHH FHH MHH FHH MHH FHH

Amhara Tigray Oromia

Poor HH Food Sources (% annual kcals) - Food Crops_Deficit crops Livestock products payment in kind purchase food aid gifts, honey

MHH FHH

Own Crop Production (kg/year) - SNP17 Poor HH

Maize - meher Sorghum - meher Maize -belg haricot beans - belg Cassava - meher Sweet potatoes - belg

17 | P a g e

Food Sources by poor FHH and poor MHH in 4 cropping sub-clusters

The picture is more complex when looking at the sub-cluster level. For instance, the table at left comprises the sub-clusters with the greatest difference between poor MHH and poor FHH in terms of the contribution of own crops to annual food energy. As seen in the table, the sub-clusters include a range of surplus to deficit zones and from highland to lowland to catchment zones. In short, there is no apparent pattern in the results shown above which include the food secure highlands sub-cluster in Amhara Region; the Western Coffee cash crop sub-cluster in

Oromia, the surplus food crops lowlands sub-cluster in Tigray; and the deficit food crops catchment sub-cluster also in Tigray.

Food Sources by poor FHH and poor MHH in sample livelihood zones

The picture is equally complex when looking at the results at the sub-cluster level by livelihood zone. In the deficit food crops cluster, typically poor FHH met the same or slightly less of their annual food needs from own crops compared to poor MHH. But there are examples of a reverse pattern. For instance, in ORM 11

(Borena Guji Maize, Wheat and Livestock zone) poor FHH secured more food energy from own crops than poor MHH (see graph at left).

The contribution of own crops to annual food needs was highest in the surplus food crops cluster but here again there was variation. For instance, in AMH 18 (Southwest Maize, Finger

Millet and Teff Productive livelihood zone) poor

FHH secured much less food energy from own crops than poor MHH. In AMH18, the principal crops are maize and finger millet for consumption, and teff for sale. Poor FHH cultivated very little of their land (0.1ha cultivated of the 0.6ha land owned). The reason seems to lie in their small household size and lack of labor although perhaps factors specific to that zone played a role as well.

FOOD SOURCES Amhara 6 LZs Oromia 8 LZs Tigray 4 LZs Tigray 4 LZs

FdSec Highlands W Coffee Surplus lowlands Deficit catchment

POOR HH MHH FHH MHH FHH MHH FHH MHH FHH

crops 73% 62% 64% 56% 56% 48% 49% 39% own milk/meat 2% 2% 2% 2% 5% 3% 1% 1% payment in kind 1% 1% 1% 0% 0% 0% 3% 2% purchase 28% 36% 38% 44% 32% 38% 29% 35% food aid 0% 1% 0% 0% 8% 8% 19% 22% other (gifts, wild food, fish etc) 0% 0% 0% 0% 0% 0% 0% 0% food summary total 104% 102% 104% 102% 100% 97% 100% 99%

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MHH FHH MHH FHH MHH FHH MHH FHH MHH FHH MHH FHH MHH FHH

ORM51 ORM11 AMH9 AMH22 TG5 TG10 SNP30

Oromia Amhara Tigray SNNPR

Poor HH Annual Food Sources - Food Crops_Deficit LZs crops LivestocL products payment in Lind purchase food aid

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MHH FHH MHH FHH MHH FHH

ORM 1 SNP17 AMH18

Food Sources (% of annual kcal) - Food Crops_Surplus crops livestock products payment in kind purchase

18 | P a g e

Results from the cash crops cluster also showed a lot of variation between the livelihood zones. In one zone, ORM12 (Borena

Guji Coffee and Enset livelihood zone), poor

FHH consumed more of their own crops than poor MHH. In this zone, maize and enset are the principal crops for home consumption and poor FHH both produced more and consumed more of these crops. By contrast, in other zones in this cluster, the more standard pattern was seen and by and large poor FHH consumed about the same or less of their own crops than poor MHH.

In sum –

Across the different clusters, poor FHH secured less annual food energy than poor MHH with an average difference of 2%.

The contribution of own crops to annual foods needs was highest in the surplus food crops cluster (55%) and lowest in the deficit food crops cluster (35%). Conversely, the contribution of food purchase to annual food needs was highest in the deficit food crops cluster (44%) notwithstanding the contribution of PSNP food transfers to food needs. This pattern holds true for poor MHH and poor FHH, reflecting the overall importance of geography, agroecology, and wealth group assets.

Own milk provides very little food energy overall to annual food needs (1%-3% of annual food needs) and the difference between poor MHH and poor FHH is minimal. The contribution of own milk was lowest in the deficit food crops cluster and highest in the Oromia food crops + livestock cluster.

surplus food crops cash crops deficit food crops FdCrops+L/stock (ORM)

POOR HH (average by cluster) MHH FHH MHH FHH MHH FHH MHH FHH crops 60% 55% 54% 50% 41% 35% 55% 52% own milk/meat 3% 2% 2% 2% 1% 1% 3% 3% payment in kind 1% 0% 0% 0% 2% 1% 2% 0% purchase 35% 38% 39% 41% 39% 44% 41% 42% food aid 3% 4% 8% 9% 17% 18% 3% 3% other (gifts, wild food, fish etc) 0% 0% 0% 0% 0% 0% 0% 0% food summary total 102% 100% 103% 102% 100% 98% 103% 101%

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MHH FHH MHH FHH MHH FHH MHH FHH

AMH 13 SNP43 ORM12 ORM33

Poor HH Food Sources (% annual kcals) - Cash Crop Cluster LZs crops livestock products purchase

19 | P a g e

Sources of Cash Income

Poor HH Annual Cash Income (ETB) - Surplus Food Crops Cluster Poor HH Annual Cash Income (ETB) - Cash Crops Cluster

Deficit Food Crops Cluster + Oromia Food Crops + L/stock Cluster

In each of the main cropping clusters, poor FHH on average earned less cash income than poor MHH.

The difference in cash income was highest in the surplus food crops cluster where poor FHH earned ETB

3,131/HH/year less than poor MHH. In the cash crops cluster, the difference dropped to ETB 2,566/HH/year and in the deficit food crops cluster the difference was even lower at ETB 1,713/HH/year. Although the cash income difference was greatest in the surplus food crops cluster, the amount earned by poor FHH (ETB

12,040/HH/year) was nonetheless higher than the amount earned by poor MHH in the deficit food crops cluster (ETB 10,017/HH/year) reflecting the low cropping potential in the deficit food crops cluster in general.

Poor FHH typically have a smaller household size than poor MHH so arguably poor FHH require less income to meet their basic needs. For this reason, it is also useful to examine per capita income results. Do income differences seen at the household level between poor MHH and poor FHH remain at the per capita level? The results by cluster and averaged by region still show that poor MHH income was typically higher than poor FHH, but the difference per capita was lower than the difference by household. For instance, in the surplus food crops cluster and the cash crops cluster, the difference per capita was about ETB 180/year, or, for a household of 5, just over ETB 900/HH5/year. This compares to the income difference per household (regardless of household size) of

ETB 3,131 and ETB 2,566 in the surplus food crops and cash crops clusters respectively. The income difference in the deficit food crops cluster dropped to about ETB 40/person/year or ETB 200/HH5/year. This compares to the calculation at the household level of ETB 1,713 in the deficit food crops cluster. In short, poor FHH on average earned less annual cash income in the reference year than poor MHH and, depending on how cash income is calculated, it ranged from roughly ETB 1,700/HH to ETB 3,100/HH per year (using wealth group household size) and from ETB 200/HH5 to ETB 900/HH5 per year (using the per capita calculation).

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MHH FHH MHH FHH MHH FHH

Amhara Tigray Oromia

Poor HH Annual Cash Income (ETB) - Food Crops_Deficit crop saLes LivestocL product saLes LivestocL saLes Labour/empLoyment seLf-empLoyment + petty trade PSNP honey, Land rentaL, credit

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MHH FHH MHH FHH MHH FHH MHH FHH

Oromia SNNPR Amhara Tigray

Poor HH Annual Cash Income (ETB) Food Crops_Surplus crop sales livestock product sales livestock sales labour/employment self-employment + petty trade PSNP honey, gifts, remittances, credit

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MHH FHH MHH FHH

Oromia SNNPR

Poor HH Annual Cash Income (ETB) - Cash Crop Cluster crop sales livestock product sales livestock sales labour/employment self-employment + petty trade PSNP honey, land rental, credit

20 | P a g e

What income sources typically generated less cash earnings for poor FHH? Income sources can be roughly divided into livestock (or animal) income; income from crop sales; and income from labor, self-employment and other (i.e., honey sales, PSNP, gifts and so on).

Poor HH Income from animals - Surplus Food Crops Cluster

On average, across the surplus food crop cluster, poor MHH earned more income from livestock sales per household than poor FHH. By contrast, poor FHH on average earned more than poor

MHH from sales of milk/ghee/butter. However, at the level of the livelihood zone, there was significant variation. For instance, one exception to the general trend was SNP17 – Gamo Gofa

Maize and Root Crops livelihood zone. In SNP17, poor FHH earned much more income from livestock sales than poor MHH. This result can be explained by specific conditions in that zone. In

SNP17, poor FHH sold more livestock, milk and butter because they cultivated less land, sold fewer crops (i.e., teff) and had less options to earn cash from labor all of which meant they had to sell livestock to generate some needed cash.

In the deficit food crops cluster, there was also significant variation across livelihood zones. For instance, AMH22 – Tekeze Lowland Sorghum and Goats livelihood zone -- was an exception to the general pattern. In this zone, poor FHH owned fewer livestock than poor MHH but had to sell an additional 0.5 sheep (or one additional sheep every two years) to make ends meet due to their challenges earning sufficient income from crops sales, labor and self-employment.

If the general rule of thumb is that poor MHH earned more annual cash income from livestock sales than poor

FHH, the other rule of thumb is that poor FHH typically earned more income from chicken & egg sales, and, at the cluster level, from livestock product sales. An example of poor household cash income from the sales of chickens and eggs is shown in the graph below at left. In terms of milk/butter/ghee sales, at the cluster level, the income difference ranged from ETB 50/HH5/year to ETB 150/HH5/year to ETB 250/HH5/year in the cash crops, surplus and deficit food crops, and food crops & livestock clusters respectively although within clusters, results varied.

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MHH FHH MHH FHH MHH FHH MHH FHH MHH FHH MHH FHH

ORM51 AMH9 AMH22 TG5 TG10 SNP30

Oromia Amhara Tigray SNNPR

Poor HH Income from animals (ETB) - Food Crops_Deficit miLL/ghee saLes LivestocL saLes egg/chicLen saLes

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MHH FHH MHH FHH MHH FHH MHH FHH MHH FHH MHH FHH MHH FHH MHH FHH

ORM1 ORM22 ORM38 SNP5 SNP17 SNP28 AMH15 AMH18

Oromia SNNPR Amhara

Poor HH Income from animals (ETB) - Food Crops_Surplus milk/ghee saLes Livestock saLes egg/chicken saLes

Poor HH Income from animals - Deficit Food Crops Cluster

1,000

1,200

1,400

ORM1 ORM22 ORM38 SNP5 SNP17 SNP28 AMH15 AMH18

Income from chicken & egg sales (ETB) - Poor HH Food Crop_Surplus

MHH FHH

NWC AMP WCS BGE JCC

AMH13 SNP3 SNP43 ORM12 ORM33

Milk/butter sales (ETB/HH/year)

MHH FHH

Poor HH Income from Chickens & Eggs - Deficit Food Crops Cluster

21 | P a g e

Another major category of cash income was crop sales. Not surprisingly, cash earnings from crop sales were significantly higher in the cash crop and surplus food crops clusters compared to the deficit food crops cluster.

Overall, across the three clusters, poor MHH earned about ETB 1,500-1,800/HH/year more than poor FHH from crop sales in the surplus food crops and cash crops clusters, and about ETB 400/HH/year in the deficit food crops cluster. Total poor MHH earnings from crop sales in the surplus food crops and cash crops clusters were ETB

6,500/HH/year and 6,800/HH/year respectively, and thus poor MHH earned 140% and 130% more than poor FHH from crop sales in the reference year. In the deficit food crops cluster, poor MHH earned only ETB 1,570/HH/year from crop sales which represented 130% more than poor FHH who only earned ETB 1,200/HH/year.

Did poor FHH earn more, or less, cash income from the sale of certain types of crops? Looking at livelihood zone data by cluster, the results revealed wide variations by zone both in types of crops sold and the amount earned from crop sales. Given this variation, it was difficult to see any typical patterns. The graphs at left show the results from several sample zones in the deficit food crops and cash crops cluster. As an example of the variation, in TG5 -- Enderta Dry Midland livelihood zone – poor FHH earned more than poor MHH from sales of teff/wheat/barley. However, in ORM11 –

Borena Guji Maize, Wheat and Livestock livelihood zone -- the opposite pattern was observed. In the cash crops cluster, poor MHH and poor FHH earned similar amounts from cash crops – such as from coffee in SNP43 Western Coffee and

Spices – but elsewhere, in ORM12

Borena Guji Coffee and Enset livelihood zone, poor MHH earned substantially more from coffee and chat than poor

FHH. Similar variations were found when looking at the data on market vegetable and spice income. Poor MHH earned more than poor FHH from sales of onions and sugar cane in AMH3 (Cheffa Valley livelihood zone) and from turmeric sales in SNP43 (Western Coffee and Spices) but in SNP3 (Alaba Mareka Lowland Pepper livelihood zone), earnings by poor MHH and poor FHH from peppers and trees were roughly the same. These variations were also noted in the surplus food crops cluster where the principal crops sold by poor households included sorghum, maize, legumes and teff but where there was no clear pattern in terms of earnings by types of crops sold and the gender of the household head.

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MHH FHH MHH FHH MHH FHH MHH FHH MHH FHH MHH FHH

ORM51 ORM11 AMH9 AMH22 TG5 SNP30

Oromia Amhara Tigray Tigray SNNPR

Poor HH Income from Crop Sales - Food Crops_Deficit LZs maize/sorghum/miLLet teff/wheat/barLey OiL crops

10000

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MHH FHH MHH FHH MHH FHH MHH FHH MHH FHH

AMH13 SNP3 SNP43 ORM12 ORM33

Poor HH Crop Sale Income (ETB) - Cash Crop LZs

Food crop sales Market vegetables, spices Cash crops (chat, coffee)

Poor HH Income from Crop Sales (ETB) - Cash Crops Cluster

Poor HH Income from Crop Sales (ETB) – Deficit Food Crops Cluster

22 | P a g e

Annual Poor HH Income from labor and other…