CDJP KIGALI

UK AID MATCH PROGRAMME BASELINE STUDY AND GENDER ANALYSIS REPORT

RASAL
CDJP Kigali

May 2016

Table of Contents

Table of Contents

I.   List of Acronyms

II.  List of Figures

III.List of Tables

1    Introduction

2    Study design

1.   Baseline Study

2.   Gender Analysis

3    Methodology

a)   Tools

1.   Baseline Study

2.   Gender Analysis

b)   Sample size and selection of respondents

c)   Data collection and analysis

4    Results and Discussion

d)   Baseline Study

i)    Demographic

ii)   Livelihoods

(1)    Household Income

(2)    Household Assets

(3)    Food Security

(4)    Farming practices

iii)  Climate Change

e)   Gender Analysis

i)    Gender Roles

ii)   Sexual and Gender Based Violence

iii)  Women Participation and leadership

iv)  Decision-Making & Control

5    Conclusions

6    UKAM2 Logframe Indicators and Targets

 

  1. I.List of Acronyms

FGD Focus Group Discussion

FHH Female-headed households

HH Household

IGA Income Generating Activities

MHH Male-headed households

SGBV Sexual and Gender Based Violence

TA Traditional Authority

 

  1. II.List of Figures

Figure 1 Gender of respondents – MW CADECOM Dedza

Figure 2 Gender of respondents - MW CADECOM Chikwawa

Figure 3 Gender of respondents – DRC CDJP Bukavu

Figure 4 Gender of respondents – Rwanda CDJP Kigali

Figure 5 Household age categories – MW CADECOM Dz

Figure 6 Household age categories – MW CADECOM Ckw

Figure 7 Household age categories – DRC CDJP Bukavu

Figure 8 Household age categories – RW CDJP Kigali

Figure 9 Main HH Income source - MW CADECOM Dz

Figure 10 Main HH Income source - MW CADECOM Ckw

Figure 11 Main HH income source - DRC CDJP Bukavu

Figure 12 Main HH income source - Rwanda CDJP Kigali

Figure 13 Income sources per household - MW CADECOM Dedza

Figure 14 Income sources per household - MW CADECOM Chikwawa

Figure 15 Income sources per household – DRC CDJP Bukavu

Figure 16 Income sources per household – RW CDJP Kigali

Figure 17 Relative land cultivation - MW CADECOM Dz

Figure 18 Relative land cultivation - MW CADECOM Ckw

Figure 19 Relative land cultivation – DRC CDJP Bukavu

Figure 20 Relative land cultivation – RW CDJP Kigali

Figure 21 Livestock types owned by respondent HHs- MW CADECOM Dz

Figure 22 Livestock types owned by respondent HHs- MW CADECOM Ckw

Figure 23 Livestock types owned by respondent HHs- DRC CDJP Bukavu

Figure 24 Livestock types owned by respondent HHs- RW CDJP Kigali

Figure 25 Assets ownership - MW CADECOM Dz

Figure 26 Assets ownership - MW CADECOM Ckw

Figure 27 Assets ownership - DRC CDJP Bukavu

Figure 28 Assets ownership - RW CDJP Bukavu

Figure 29 % HHs consuming food groups at least once a week - MW CADECOM Dz

Figure 30 Main food sources - MW CADECOM Dedza week - MW CADECOM Dz

Figure 31 % HHS consuming food group at least once a week - MW CADECOM Ckw

Figure 32 Main food sources - MW CADECOM Ckw

Figure 33 % HHs consuming food groups at least once a week - DRC CDJP Bukavu

Figure 34 Main food sources - DRC CDJP Bukavu

Figure 35 % HHs consuming food group at least once a week - RW CDJP Kigali

Figure 36 Main food sources - RW CDJP Kigali

Figure 37 % Households stating to be food insecure – MW CADECOM Dedza

Figure 38 % Households stating to be food insecure – MW CADECOM Chikwawa

Figure 39 % Households stating to be food insecure – DRC CDJP Bukavu

Figure 40 % Households stating to be food insecure – RW CDJP Kigali

Figure 41 Most valuable farming practices - MW CADECOM Dedza

Figure 42 Most valuable farming practices - MW CADECOM Chikwawa

Figure 43 Most valuable farming practices – DRC CDJP Bukavu

Figure 44 Most valuable farming practices - RW CDJP Kigali

Figure 45 Results on two statements on gender roles - MW CADECOM Dz

Figure 46 Results on two statements on gender roles - MW CADECOM Ckw

Figure 47 Results on two statements on gender roles – DRC CDJP Bukavu

Figure 48 Results on two statements on gender roles – RW CDJP Kigali

Figure 49 SGBV attitudes - MW CADECOM Dedza

Figure 50 SGBV Attitudes - MW CADECOM Chikwawa

Figure 51 SGBV Attitudes – DRC CDJP Bukavu

Figure 52 SGBV Attitudes - RW CDJP Kigali

Figure 53 Membership of women to Village Development Committee – CADECOM Dz

Figure 54 Type of participation of women to Civil Protection Committee – CADECOM Ckw

Figure 55 Results of statements 1 and 8 on decision making and control - MW CADECOM Dz

Figure 56 Results of statements 1 and 8 on decision making and control - MW CADECOM Ckw

Figure 57 Results of statements 1 and 8 on decision making and control - DRC CDJP Bukavu

Figure 58 Results of statements 1 and 8 on decision making and control - DRC CDJP Kigali

  1. III.List of Tables

Table 1 UK Aid-Match Impact, Outcome and Expected Outputs

Table 2 Target Impacts, Indicators, Draft Questions and Tools

Table 3 CADECOM Chikwawa Target HHs Survey sample per village

Table 4 CADECOM Dedza Target HHs Survey sample per village

Table 5 CDJP Bukavu Target HHs Survey sample per village

Table 6 CDJP Kigali Target HHs Survey sample per village

Table 7 Sample size for Community HH Survey by country, partner and village

Table 8 Seasonal calendar for IGAs and farming activities

Table 9 Average cases of SGBV reported in Malawi Ntcheu district (Malawi)

Table 10 Average cases of SGBV reported in Nsanje district (Malawi)

Table 11 Average cases of SGBV reported in Mulamba district (DRC)

Table 12 Average cases of SGBV reported in Bugesera Rwanda (Rwanda)

Introduction

The UK Aid-Match is a DFID-funded 3-year multi-country integrated programme implemented in the DRC, Rwanda and Malawi through 4 implementing partners, CDJP Bukavu in the DRC, CDJP Kigali in Rwanda and CADECOM Dedza and CADECOM Chikwawa in Malawi. The programme aims at building the asset base of the extreme poor and their capacities to improve food and income security as well as linking smallholder farmers to markets and other services providers, to increase community resilience against the shocks caused by climate change and to improve the socio-economic conditions of women. Women are a key focus of the programme with outcomes targeting women's empowerment and women's participation in decision-making structures. The programme will work with local leaders - both customary and government - to expand appreciation for the benefits of women's empowerment for households (HHs), the community and the nation.

The programme is intended to target 2,000 small-scale farming households in 25 villages in three countries DRC, Rwanda and Malawi. Criteria for beneficiary households’ selection are the following:

  • female-headed households
  • child-headed households
  • elderly-headed households
  • SGBV survivors
  • HIV+ infected and chronically ill
  • extreme poor households

Overall, beneficiary households live in food insecure conditions, vulnerable to natural disasters and unable to absorb external shocks due to the low level of assets at their disposal. The UK Aid-Match programme objective is to improve the well-being of 2,000 marginalised and extreme poor households through four specific programme outputs (1) increased food and income security (2) reduce the vulnerability of households in the face of climate change (3) gender equality (4) improved access to services and links with markets for smallholders farmers. Programme impact, outcome and key outputs planned for the programme are shown in Table 1.

Table 1 UK Aid-Match Impact, Outcome and Expected Outputs

Programme Impact: Improved resilience and reduced extreme poverty of targeted vulnerable households in 25 villages of DRC, Malawi and Rwanda.
Programme Outcome: Increased food and income security, gender equality and resilience to withstand and thrive despite climate shocks of 2,000 vulnerable farming households, (mainly female-headed households) of 25 villages of DRC, Malawi and Rwanda.
Output 1 (Food & Income) Targeted small-scale farming households have increased their food and income security through better access to productive assets, adoption of sustainable farming practices and diversified income sources.
Output 2 (DRR & Climate Change Adaptation): Target communities become less vulnerable to the impacts of climate change by developing and implementing community-based disaster adaption and mitigation strategies.
Output 3 (Gender and Women Empowerment) Targeted small-scale women farmers are able to access and control over resources within the households and in the wider community with increased public participation and leadership positions.
Output 4 (Advocacy & Farmers Associations) Male and female small-scale farmers are able to engage with state and non-state actors that impact on their livelihoods and are better able to access services and markets through farmers' associations.

The Logical Framework is the key document defining programme impact, outcomes, outputs, milestones, activities and timeframe for data collection. The Monitoring Evaluation and Learning (MEL) Plan explains in detail the overall monitoring and evaluation system, the processes and tools required to monitor activities and assess results and impacts of the programme.

The Baseline Study, whose results are provided in this report, has been the first process of data gathering and the main purpose was to establish the benchmarks against which programme’s milestones and results will be measured throughout programme duration. Therefore, the information collected is directly linked with the set of indicators established for the programme, such as households’ incomes and sources, food security, levels of decision-making power for women, awareness creation about climate change, etc. In addition to that, considered that the UKAM programme focuses on women and accordingly these are the large majority of beneficiaries, gathering information on women conditions and gender equality status in targeted communities was deemed necessary and hence a Gender Analysis has been conducted and likewise results are reported here.  

2          Study design

The Baseline Study and Gender Analysis have been conducted contemporary in the areas of intervention of the programme in all three countries during February and March 2016. This report provides a summary of the results of both enquiries.

  1. Baseline Study

The specific objectives of the baseline study exercise is to gather information:

  • To serve as a baseline for assessing programme impact: through the baseline study, benchmarks will be established to define the onset situation and to measure variations that will occur during programme implementation.
  • To review the milestones and targets defined in the Logframe: as a key programme documents the Logframe established targets sought to be achieved throughout programme implementation. However some of them are still missing from the Logframe as they require an actual data collection exercise, and this will be done through the Baseline study.    
  • To break down targets by country and by gender: the initial Logframe established general targets common for all 3 countries. However, because socio-economic contexts in the DRC, Rwanda and Malawi are different, benchmarks are likely to be different as well, and therefore targets will also need to capture such variances. For accuracy of information hence it was agreed that following the analysis of information gathered during the Baseline study, separate targets and milestones will be established for each country. The revised and compiled Logframe will be shared with DFID in April 2016.
  • To share data and information collected including analysis with the implementing partners, programme beneficiaries and concerned government authorities: for their knowledge and meaningful involvement in the programme implementation. A copy of the baseline study will be shared with the DFID.      

The table 2 below summarises programme’s expected results, specific indicators, related draft questions and tool to gather the information required:  

Table 2 Target Impacts, Indicators, Draft Questions and Tools

Targeted Impact Impact Indicator Draft Questions Tool
Improved resilience and reduced extreme poverty of targeted vulnerable households in 25 villages of DRC, Malawi and Rwanda. Number of targeted small-scale farming households (female and male headed) able to earn more than $1.25 per day.

What is the annual income of this HH?

(Breakdown by income sources)

Targeted Household Survey
Targeted Outcome Outcome Indicator Draft Questions Tool
Outcome 1: Increased food and income security, gender equality and resilience to withstand and thrive despite climate shocks of 2,000 vulnerable farming households, (mainly female-headed households) of 25 villages of DRC, Malawi and Rwanda.   1.1 No of households stating to be food secure* for at least 10 months in the last year.     (*Food secure is defined as the ability to eat at least two meals a day. It is based on Food and Agriculture Organisation's revised definition in 2001.) For each of the months indicated below, did you (specifically respondent the head of the household) have less than two meals per day for at least a continuous week within the month? Targeted Household Survey
1.2 % increase of household’s annual income among target households.

How much cash income from these sources has the household earned in the last year (local currency)?

1.3 % of women who have increased access to, control over, and ownership of assets and income.

Two selected statements:

1. For cash income earned specifically from you, who decides how to use the money?

2. For a small livestock to be sold for any reason, who decides if and when to sell it?

Community Household Survey; Focus Group Discussion
1.4 % of community members able to mention at least 3 major climate related risks which are affecting them and know at least one coping strategy to mitigate those risks.

What are the most important risks or hazards related to climate change? And could you mention any coping strategy you adopt?

Community Household Survey;

Focus Group Discussion

Targeted Output Output Indicator Draft Questions Tool

Output 1:   Targeted small-scale farming households have increased their food and income security through better access to productive assets, adoption of sustainable farming practices and diversified income sources.

1.1.a: No of targeted small-scale farmers (female and male) trained in sustainable farming practices.

NA

Partners Quarterly Report; Participants Training List
1.1.b: % & no of trained small-scale farmers (female and male) have adopted at least two improved farming practices each from crop and soil management and soil erosion prevention methods.

NA

Adoption survey
1.2.a: No of targeted small-scale farmers (female and male) supported on developing income generating activities.

NA

Partners Quarterly Report
1.2.b: % and no of targeted small-scale farmers (female and male) who have more than two* income sources (* this indicator might be adjusted according to baseline results). What sources of income have you had in the last year (see full list in the survey)? (tick as many as necessary) Targeted Household Survey
1.3 % of targeted farming households (female and male headed) are members of community seed bank in targeted 25 villages.

NA

Partners Quarterly Report; Activity Tracking Sheet
1.4 No of targeted small-scale farmers (female and male) that a) receives small livestock and b) trained in livestock management.

NA

Activity Tracking Sheet;

Partners Quarterly Report

1.5 No of targeted small-scale farmers benefitting from irrigation facilities or water supplies.

NA

Partners Quarterly Report; Activity Tracking Sheet;
Output 2:   Target communities become less vulnerable to the impacts of climate change by developing and implementing community-based disaster adaption and mitigation strategies. 2.1 Number of village disaster risk reduction (DRR) management committees have been established and trained on DRR.

NA

Partners Quarterly Report; Activity Tracking Sheet
2.2 Number of village based disaster adaption and mitigation strategies developed and implemented.

NA

Partners Quarterly Report; Activity Tracking Sheet;
2.3 At least two action plans are implemented jointly with related local government from village based disaster adaption and mitigation strategies.

NA

Partners Quarterly Report; Partners Quarterly Report
Output 3:   Targeted small-scale women farmers are able to access and control over resources within the households and in the wider community with increased public participation and leadership positions. 3.1 No of targeted small-scale farmers (female and male) trained on gender awareness.

NA

Partners Quarterly Report; Activity Tracking Sheet
3.2.a: No of targeted women farmers trained on leadership skills.

NA

Partners Quarterly Report; Activity Tracking Sheet
3.2.b: No of targeted women farmers in leadership position. Do you belong to any community committees or associations? Are you a simple member or a member of the steering committee? Community Household Survey
3.3 Reduction in incidence of sexual and gender based violence among targeted farming households.

a. reported cases of wives being beaten by their husbands

b. % of men AND women answering that is SOMETIMES acceptable for a husband to beat his wife if she goes somewhere without his approval.

Community Household Survey; Focus Group Discussion
Output 4: Male and female small-scale farmers are able to engage with state and non-state actors that impact on their livelihoods and are better able to access services and markets through farmers' associations. 4.1 Number of farmer associations formed with minimum membership of 50 farmers per farmer associations.

NA

Partners Quarterly Report; Activity Tracking Sheet

4.2 Number of services extended by farmer associations for their members.

To be developed for programme Mid-Term Review

Farmers Associations Survey

Among all programme indicators only those highlighted in grey in the table above have been assessed through the Baseline Study as they are higher level indicators capturing medium and long term results of the programme. For the remaining indicators the information will be collected on an on-going basis through other tools such as Quarterly Reports, Activity Tracking Sheet, Training attendance list, etc. and therefore the draft questions is not considered (NA).    

According to the indicators reported above, the baseline study gathered the following information: (refer to the attached Programme Logframe for further details)

  • Food provisioning of targeted households, represented by women and men
  • Diversity of food consumption and hunger gap period
  • Annual average income of targeted households from farm and non-farm activities
  • Irrigation facilities in village
  • Livestock situation
  • Human, social and physical capital of participating households representing women and men
  • Main shock from climate change and coping ability of the community members from different shocks
  • Innovative farm technologies and its adoption
  • Access to services and inputs from various service providers that should include access to safe drinking water and sanitary condition
  • Reducing vulnerability by adapting appropriate preventive measures against shocks and stresses
  • Level of women decision-making and control over access and resources in the households and wider community
  • Level of participation and leadership of women in community structures
  • Level and impact of SGBV for women
  1. Gender Analysis

This study is among the activities foreseen in the programme in Year 1. It was deemed necessary to analyse the context in which the programme will operate, and will help SCIAF and the implementing partners staff to understand gender roles, responsibilities, statuses and inequalities so that we will be able to use that information to design, implement, monitor and evaluate programmes activities on gender. The gender analysis was framed around the following list of research questions to guide the inquiry:

  1. What are current gender roles and responsibilities of both men and women within the household? What are perceptions on gender roles in the wider communities?
  2. What is the decision-making power of women and men, and their access and control of assets and resources in the household?  
  3. What is the level of participation and leadership of women in the community?  
  4. What is the level of Sexual and Gender Base Violence (SGBV) in the household and the wider community, and which are its causes and likely implications for women empowerment?

3          Methodology

The baseline study and gender analysis were conducted contemporarily during February and March 2016, in all 25 villages of the UK Aid-Match programme intervention. The section below provides more information on the tools utilised for both inquiries, the sample and size of respondents and overall how the data collection was actually undertaken.  

  1. Tools
  2. Baseline Study

The information for the baseline study, mentioned above, was gathered through the following tools:

  • Target Household Survey done on a random sample of households targeted by the programme in each village. Respondent of this survey is the head of the household.
  • Community Households Survey with randomly selected households (not necessarily programme beneficiaries) in targeted villages. This survey has been carried out separately with males and/or females most senior member of the household (e.g. husband and wife separately).
  • Focus Group Discussion (FGD) on Livelihoods mixed group of women and men on major wealth ranks in the community, households assets, IGAs, income sources, seasonal incomes, livelihoods options, etc.    
  • Focus Group Discussion (FGD) on Climate Change with community members and local government officials to understand climate change impact on the livelihoods of people in the community; which socio-economic categories are more negatively affected and which coping strategies people have put in place to mitigate the impact of climate change.  

Instructions:one FGD/village. FGD participants are from both target and non-target HHs.

  • Secondary data and national statistics on poverty mainlyto triangulate the data gathered during the households questionnaires on household income and poverty levelsin target countries
  1. Gender Analysis

The 4 research questions outlined above have been addresses using the following tools:

  • Community Households Survey with randomly selected households (not necessarily programme beneficiaries) in targeted villages. This survey has been carried out separately with males and/or females most senior member of the household (e.g. husband and wife separately)
  • Focus Group Discussion (FGD) on gender roles, decision-making, leadership, SGBV with separate groups of men and women. The aim is to understand gender practices, share of work load and tasks and responsibilities, who does what in the household (activity profile); norms and constraints in the community for women empowerment; decision-making, participation and leadership for women; control of assets and resources incidence and causes of SGBV.

As it can be noticed, the tools utilised for both baseline study and gender analysis are similar and sometime overlapping. For instance the Community Household Survey assisted in gathering information for both baseline study and gender analysis.  

  1. Sample size and selection of respondents

Because the Baseline study aimed at conducting two surveys for two different “populations” - a survey for targeted beneficiaries and a survey for the whole community - two different sample size were calculated. Both surveys are statistically significant and the sample size were calculated with an online calculator (http://www.raosoft.com/samplesize.html). The Target Households Survey is statistically significant with 5% margin of error and 95% confidence level for each of the partners as all partner apart from one conducted at least 217 HHs - considering a population of 500 beneficiaries per partner. Only CADECOM Dedza conducted 181 surveys thus achieving only 90% of confidence level. The Community Household Survey is only statistically significant for the overall population of 3,880 households in all 25 villages, rather than statistically valid by partner, with 5% margin of error and between 93% and 94% confidence level. Overall partners conducted a total of 317 Community HHs surveys.

For the Target HHs Survey information on sample size per partner and village are summarised in the tables below:

Table 3. CADECOM Chikwawa Target HHs Survey sample per village

Village N. of beneficiary HHs N. of sampled HHs
Dick 50 21
Fombe 55 26
Mapeperele 99 38
Chibuli 1 120 64
Chibuli 2 66 30
Mayang'ana 110 38
TOTAL 500 217

Table 4 CADECOM Dedza Target HHs Survey sample per village

Village N. of beneficiary HHs N. of sampled HHs
Daule 80 25
Devete 40 18
Kaudza 160 50
Kazembe 80 29
Potolani 40 17
Zuze 100 42
TOTAL 500 181

Table 5 CDJP Bukavu Target HHs Survey sample per village

Village N. of beneficiary HHs N. of sampled HHs
Kalengera 72 31
Kangala 71 31
Midhua 70 31
Mubondwe 71 31
Mudhiri 70 31
Njove 76 32
Nshimbi 70 31
TOTAL 500 218

Table 6 CDJP Kigali Target HHs Survey sample per village

Village N. of beneficiary HHs N. of sampled HHs
Kabumbwa 81 36
kamugera 82 36
Karama 85 37
Nyakayenzi 86 37
Rubirizi 82 36
Twimpala 84 37
TOTAL 500 219

Some partners sampled an equal number of households in all village, while others did because they reflect the actual number of beneficiary households targeted per village, which is related to the size of the village.  

For the Community HHs Survey considering a total population of 3,880 HHs in all 25 villages, the 317 HHs conducted by partners are statistically significant with 5% margin of error and between 93% and 94% confidence level.

The table below shoes the break down by country and partner:

Table 7. Sample size for Community HH Survey by country, partner and village

Country Partner Community HHs Survey/Partner Community HHs Survey/Village
DRC CDJP Bukavu 70 HH Surveys 10 Survey x 7 villages
Rwanda CDJP Kigali 60 HH Surveys 10 Survey x 6 villages
Malawi CADECOM Dedza 115 HH Surveys 19 Survey x 6 villages
Malawi CADECOM Chikwawa 72 HH Surveys 12 Survey x 6 villages
Total   317 HH Surveys 250 HH Surveys

The sample of households to be surveyed with the Target HHs Survey have been randomly chosen from the full beneficiaries list. This sample represents approximately the 44% of whole population, hence partners picked names in the list one time every 2 and one time every 3 households in the list until 218 HHs were selected. Instead for the Community HHs Survey where the population is the entire community, households to be surveyed have been randomly chosen through a transect walk.

Each partners have also conducted Focus Group Discussions in each of the area if inquiry, namely Livelihoods, Disaster Risk Reduction and gender in various villages. Each partner conducted:

  • 3 FGD on Livelihoods in three different among targeted villages
  • 3 FGD on Climate Change & DRR in three different among targeted villages
  • 3 FGD on Gender (with women) in three different among targeted villages
  • 3 FGD on Gender (with men) in three different among targeted villages
  1. Data collection and analysis

Each partner established a team of 9 people (approx.) to carry out the Baseline exercise and gender analysis composed by:

  • Local Coordinator: to work hand in hand with the Programme Manager on the overall organisation of the Baseline exercise; to closely monitor and supervise the process; to lead the induction training for staff in preparation of the data collection in the field in collaboration with SCIAF PO  
  • 1 Programme Manager (Team Leader): to supervise the overall process and accompany staff during the field work, check the quality of the Baseline study procedure, organise the team and data collection in the field, accommodation and materials, equipment required, etc.
  • 3 Field Staff: to conduct the Community HHs Survey and FGD with community members; to supervise data collectors; to support data collectors in conducting HHs surveys (if necessary)
  • 6-7 Data Collectors: to conduct Target HHs Survey

Before starting the actual data gathering exercise, all team member (above mentioned) have been trained over a 2-day induction training on “Baseline Study and Gender Analysis Techniques for Data Gathering Fieldwork”. SCIAF Programme Officer and the two Local Coordinators facilitated the training. This aimed at refreshing participants of basic data collection techniques, familiarise with the specific tools developed for these inquiries and to plan the actual data gathering exercise. Details of this training can be found in Agenda and Guidelines of the training.

The whole process took a period of 4-6 weeks comprising data collection, data quality check and data entry. Below a summary of each partner’s fieldwork:

CADECOM Dedza conducted the inquiry from 1st February to 4th March 2016 in Njolomole Traditional Authority (TA), Ntcheu District in six villages namely Potolani, Daule, Kaudza, Devete, Zuze and Kazembe. Overall, 181 Target HH Surveys, 115 Community HH Survey and 12 FGDs were conducted, the latter through both mixed groups (for the livelihoods and DRR discussions) and women and men-only groups (for the discussions on gender).

CADECOM Chikwawa conducted the inquiry between the 15th February and 2nd March 2016 in Ndamela TA, Nsanje District. Six Villages were surveyed from Group Village Headman Chibuli, thus Chibuli, Chibuli 2, Dick, Mapeperere, Fombe and Muyang’anira Villages. Overall, 217 Target HH Surveys, 72 Community HH Survey and 14 FGDs were conducted, the latter through both mixed groups (for the livelihoods and DRR discussions) and women and men-only groups (for the discussions on gender).

CDJP Bukavuconducted the inquiry between the 17th February and 4th March 2016 in Mulamba District in the seven targeted villages Njove, Kangala, Mubondwe, Kalengera, Miduha, Nshimbi and Mudirhi. Overall, 217 Target HH Surveys, 70 Community HH Survey and 10 FGDs were conducted, the latter through both mixed groups (for the livelihoods and DRR discussions) and women and men-only groups (for the discussions on gender).

CDJP Kigaliconducted the inquiry between the 22nd February and 11th March 2016 in Bugesera District, Nyakayenzi and Rutonde Cells in six targeted villages Rubirizi, Kabumbwe, Twimpala, Kamugera, Karama and Nyakayenzi. Overall, 219 Target HH Surveys, 60 Community HH Survey and 12 FGDs were conducted, the latter through both mixed groups (for the livelihoods and DRR discussions) and women and men-only groups (for the discussions on gender).

After the field work was completed, data analysis followed. The partner led on the analysis of the results from the FGDs, while SCIAF led on the statistical analysis of the survey questionnaires, along with compiling the whole baseline and gender analysis report.

The following section is a summary of the findings of both inquiries and results are presented separately between baseline study and gender analysis to report consistently on both studies.      

4          Results and Discussion

  1. Baseline Study

The Target Household Survey was structured in five main areas of inquiry: the demographic and household composition; household assets; household incomes; food security and farming practices. In addition to these areas, the Community Household Survey provided further information on the impact of climate change and other gender-related inquiries such as gender roles, decision making, women leadership and SGBV, the latter being reported under the Gender Analysis section.  

  1. Demographic

The information below is obtained from the Target HHs Survey and therefore it refers to the beneficiaries sample, rather than the overall population.

Malawi – CADECOM Dedza: The large majority of households questionnaires respondents were women (82.3%) (Fig. 1), of which 53% are also head of the household (Fig. 1). The average age of all respondents is 49 years.

Malawi – CADECOM Chikwawa: The majority of households questionnaires respondents were men (58%) (Fig. 1) and among the remaining 42% women 59% were head of the household (Fig. 2). The average age of all respondents is 45 years.

DRC – CDJP Bukavu: The large majority of households questionnaires respondents were women (83.9%) (Fig. 3), of which 79% are head of the household. The average age of all respondents is 53 years.[E1]

Rwanda - CDJP Kigali: The majority of households questionnaires respondents were also women (79.5%) (Fig. 4), of which the majority (57%) are spouses of the head of the household. The average age of all respondents is 46 years.

In terms of household composition and age categories the graphs below represent the share of the various age categories within the household.

Malawi – CADECOM Dedza:

Children and adolescents (between 6 and 18 years old) is the most represented age category with 38.4% as a combined value for female, male, natural and dependents, followed by young adults both male and female between 19 and 35 years old (18%) (Fig.5). Overall it was found that 14.4% of all households’ members are orphans or dependents. Respondents were inquired whether they had any chronically hill or disabled in the household (both mental and physical) and 35% were found to have at least one disabled or chronically hill person.

Malawi – CADECOM Chikwawa:

Children and adolescents (between 6 and 18 years old) is the most represented age category with 35% as a combined value for female, male, natural and dependents, followed by children below the age of 5 (both natural and dependent) with 25%, young adults between 19 and 35 years old (19%), adults below 55 years (13%) and people above 55 years old (8%) (Fig. 6). Overall it was found that 13% of all households’ members are orphans or dependents.

DRC – CDJP Bukavu:

Children and adolescents (between 6 and 18 years old) is the most represented age category with 44% as a combined value for female, male, natural and dependents, followed by children below the age of 5 (21%), young adults (13%), adults (12%) and people aged above 55 years old (10%) (Fig. 7). Overall it was found that 9.3% of all households’ members are orphans or dependents. Respondents were inquired whether they had any chronically hill or disabled in the household (both mental and physical) and 23.5% were found to have at least one disabled or chronically hill person in the household.

Figure 7 Household age categories – DRC CDJP Bukavu

Rwanda - CDJP Kigali:

Children and adolescents (between 6 and 18 years old) is the most represented age category with 39% as a combined value for female, male, natural and dependents, followed by young adults (20%), children below the age of 5 (18%), adults (14%) and people aged above 55 years old (9%) (Fig. 8). Overall it was found that 8.2% of all households’ members are orphans or dependents. 57% of respondent households were found to have at least one disabled or chronically hill person in the household.

  1. Livelihoods[E2]
  1. Household Income

The information gathered through the household surveys on the sources of incomes the households had during the last year and the amount earned from each of them, directly link to three of the Logframe indicators:

Impact Indicator (II) 1: Number or % of targeted small-scale farming households (female and male headed) able to earn more than $1.25 per day.

Outcome Indicator (OI) 1.2: % increase of household’s annual income among target households.

Output Indicator (oI) 1.2.b: % and no of targeted small-scale farmers (female and male) who have more than two* income sources

In addition to data being disaggregated by country, it was also disaggregated by gender. Since the inquiry concerned incomes from the whole household, answers were analysed separately between female respondents who are also head of their households and all other respondents (male head of the household and female not head of the household). For this reason two figures are found for each indicator.

Malawi – CADECOM Dedza

0% of FHH and 1% of MHH were found to earn more than 1.25$/day during the last 12 months (Impact Indicator 1)and the average household annual income reported is 28,832MK (42$) for FHH and 40,513MK ($59) for MHH (Outcome Indicator 1.2). For further details on average incomes by source see Annex 1, and on average daily incomes see Annex 5. These results seemed quite low, however these were compared with secondary information, namely national statistics carried out in Malawi (Integrated Household Survey 2010-2011), where poverty line was calculated based on local costs for the provision of a basic food and non-food items basket for a household. It is reported that “population that has total consumption below MK37002 ($52) is deemed poor and the population with total consumption less than MK22956 ($32) is considered ultra-poor”. It should be also considered that Ntcheu and Nsanje Districts, the areas of intervention of the UK Aid-Match programme, are amongst the most impoverished districts in Malawi, and additionally the survey was carried out with households targeted by the programme, hence the poorest and most vulnerable ones. This actually provides strong justification to the findings of the baseline study.  

In terms of number of income sources we found that households have on average 2.4 income sources and that 34% of FHH and 41% of MHH respondents have more than two income sources (Outcome Indicator 1.2b). For data on number of income sources see Annex 5.

Malawi – CADECOM Chikwawa

0% of FHH and 4% of MHH were found to earn more than 1.25$/day during the last 12 months (Impact Indicator 1) and the average household annual income reported is 37,493MK ($54) for FHH and 58,411MK ($84) for MHH (Outcome Indicator 1.2). For further details on average incomes by source see Annex 1, and on average daily incomes see Annex 5. For triangulation purposes see explanations for CADECOM Dedza above.

In terms of number of income sources it was found that in this area households have on average 2.2 income sources and that 44% of FHH and 36% of MHH respondents have more than two income sources (Outcome Indicator 1.2b). For data on number of income sources see Annex 5.

It should be noticed that in the updated programme Logframe data from the two partners in Malawi on household income and number of income sources have been aggregated into consolidated values for the whole Malawi, based on average values.

DRC – CDJP Bukavu:

1.4% of FHH and 1.4% of MHH were found to earn more than 1.25$/day during the last 12 months (Impact Indicator 1) and the average household annual income reported is 82,576Fc ($89) for FHH and 82,576Fc ($72) for MHH (Outcome Indicator 1.2). For further details on average incomes by source see Annex 1, and on average daily incomes see Annex 5. Unfortunately no relevant reports were found for DRC to triangulate results with secondary data.

In terms of number of income sources it was found that in this area households have on average 1.6 income sources and that 13% of FHH and 18% of MHH respondents have more than two income sources (Outcome Indicator 1.2b). For data on number of income sources see Annex 5.

Rwanda - CDJP Kigali:

0% of FHH and 2% of MHH were found to earn more than 1.25$/day during the last 12 months (Impact Indicator 1) and the average household annual income reported is 37,412RF ($48) for FHH and 60,702RF ($78) for MHH (Outcome Indicator 1.2). For further details on average incomes by source see Annex 1, and on average daily incomes see Annex 5. This information was triangulated with available national statistics and surveys carried out in Rwanda reporting average annual households incomes and poverty lines. According to the Rwanda Poverty Profile Report (2014), the reported poverty line figure based on a basic food and non-food items basket is equal $0.58/day ($212/year), so well below the standard poverty line figure of $1.25/day. Therefore the findings from this study seem plausible and in line with other national studies.

In terms of number of income sources it was found that in this area households have on average 1.6 income sources and that 8% of FHH and 20% of MHH respondents have more than two income sources (Outcome Indicator 1.2b). For data on number of income sources see Annex 5.

In both FGDs and household questionnaires, we have enquired about the main income source for the household and results across the different countries show a similar trend: piecework/farm labour and sale of farm produce are the main sources of income, followed by small business and petty trade.

In Malawi the main crops sold are maize, beans, sweet potato, potato, various types of vegetables, tomatoes, tobacco, sugar cane and bananas. In DRC main agriculture produces sold are sweet potato, banana, maize, sorghum, vegetables and other fruits and similar products are sold in Rwanda too. s[E3]

In Malawi, namely in target villages in Ntcheu District, 51% of respondents provide farm labour as source of income, 30% sell crops and 7% are engage with small business and petty trade. Also salary from NGOs or similar in the form of social cash transfer to few very poor households administered through the council, is quite a relevant source mentioned by 6% of households. In target villages in Nsanje District, crops sale and sale of processed foods and products are carried out respectively by 28% of respondents each, whereas only 21% of households provide farm labour as a source of income. 11% of respondents receive cash transfers from government or NGOs.  

In DRC Mulamba District, 36% of households provide labour as a source of income, 34% sell crops, 15% engage in small business or petty trade and 11% sell processed foods or products, such doughnuts made from wheat or maize flour locally called “chitumbuwa (also in Malawi) or boiled sweet potato or groundnuts sold in the local markets. In Rwanda, the large majority relies on the provision of farm labour to earn cash (49% of respondents), 23% sell crops, 9% engage with small business and petty trade. The 23% of respondents answering “Other” referred to saving and lending groups, and again to the provision of farm labour and sale of crops, hand crafts such as making baskets, mats, small business, sale of sorghum beer, sale of small cattle and community work paid by the government (Vision 2020 Umurenge Program, VUP).[E4]                                            

Figure 9 Main HH income source - Malawi CADECOM Dedza

In addition to the primary source of income at household level, we have also inquired about all income generating activities (IGAs) that contribute to the overall household income. From both households survey and the FGDs carried out in Malawi (Dedza) it emerged that piece work (ganyu), sale of maize, vegetables and groundnuts, engaging in small scale business, for instance of firewood and reeds are among most relevant income sources[E5]. In the FGDs it was highlighted how the piecework and selling of firewood may have negative long term impacts. Piece work cause a reduction in the availability of labour in own farms with implications of lower food production leading to food insecurity.[E6] Selling firewood is mainly done during the lean period in order to buy food but this has obviously serious environmental impacts and also mentioned as a cause of more frequent flooding in the area and country as whole. Also, this activity does not seem very profitable as the proportion of enterprises selling forest based products declines as one moves upward along the quintiles from 27 percent in the lowest to 8 percent in the highest quintile (Integrated Household Survey 2010-2011, 2012). Similarly, in the FGDs was highlighted that small-scale sales of on-farm and off-farm products such as fried scones, tomatoes, other vegetables or fruits in the targeted communities are normally based on small capital and thus do not yield significant profits. [E7]

Fig. 25 and 26 below report findings from the households questionnaires on most common income sources in the household in Malawi, which are in line with the findings from the FGDs.  

In DRC[E8], an additional source of income is the manual digging of mineral resources such as gold, casserite and coltan, mainly by young men able to migrate in resources-rich areas, while older men normally stay in the village and engage in agriculture or livestock rearing. Also here piecework is a major source of cash, done by the 41% of respondent households, and mainly among women with no capital at their disposal[E9], while 34% of respondents sell their crops or engage in petty trade in little shops or at the local markets, mainly those with some financial means. In Rwanda selling of agricultural products is a main income source. The charts below (Fig. 27 and 28) show findings from households questionnaires on main income sources in the household.

So what does this all show – well the amount that people are getting from selling their crops is not sufficient so they engage in piecemeal labour. What does crop diversity say. Are the households with more income sources more resilient – this is what the indicator implies[CB10].

One issue shared by FGD participants is that farmers are not organised in groups like associations nor cooperatives to collectively sell their produce. For this reason farmers have no bargaining power for their produce and they realize very little across the value chain of their own production and as one reported “lack of markets contribute to chronic poverty and food insecurity even reduces farmers’ interests to grow certain crops”. [E11]

The below table reports break down by country and by month of main IGAs and farming activities undertaken throughout the year [E12]

Table 8 Seasonal calendar for IGAs and farming activities

  Malawi DRC Rwanda
Jan Sale firewood; Farm labour; Sale of grass; Start of VSLs scheme small scale business , sale of grass, pièce work (farm labour)

Sale of handcrafts (mats, baskets) Farm labour, piece of work,

VSLs schemes, sale of small livestock, sale of banana beer, sorghum beer, small scale business.

Weeding, banking, basal application of fertilizer, top dressing fertilizer application, transplanting rain-fed tomato Season B : land clearing and preparation

Season A: Harvest of beans, soybean, short cycled maize

Season B: sow sorghum

Feb Sale firewood; Sale of grass; VSLs scheme Sale of grass and reed VSLs scheme, small scale business, farm labour, sale of small livestock,

Land preparation in lowland field, planting peas, nursery sowing for vegetables, onions and tomato

Season B: Sowing of short-cycled crops Prepare land for beans cultivation; to weed sorghum
Mar Sale of rain-fed harvest produce; VSLs scheme; small scale business Food for work (farm labour) Sale of small livestock (goats, sheep, hen, rabbits), piece wok, farm labour, small scale business, VSLs scheme
Land preparation in lowland field, planting peas, nursery sowing for vegetables, onions and tomato, Harvesting beans, selling sweet potato Season B: Sowing of short-cycled crops, weeding Sow beans, maize, soya beans, groundnuts; to cultivate
Apr Sale of rain-fed harvest produce; Sale of pottery; VSLs scheme; small scale business Small scale business and sale of harvest production Sale of small livestock, piece work, VSLs scheme
Land preparation in lowland field, planting peas, nursery sowing for vegetables, onions and tomato, Harvesting beans, selling sweet potato Weeding Weed beans and maize
May Sale of pottery; VSLs scheme; small scale business Selling of vegetables Sale of small livestock, piece wok, small scale business, VSLs scheme
Slashing and planting crops in lowland fields, selling garden crops, harvesting field crops. Season A and B: crops harvest, land preparation in lowland fields No intense activities related to agriculture
Jun Sale of crops (maize, soya, tomato, vegetables and sugarcane) Farm labour; Sale of pottery; VSLs scheme; small scale business. Sale of crops (maize, beans, vegetables, cabbage) Sale of crops (maize, beans, vegetables, tomatoes, carrots) Sale of small livestock, piece wok, VSLs scheme
Garden clearing, harvesting crops (maize, soya, millet), mowing grass for thatching house Land preparation in lowland fields Harvest beans, soya beans and Piecework
Jul Sale of vegetables and other field crops; Farm labour; Sale of pottery; VSLs scheme; small scale business

artisanal dig minerals,

pay work in marshland

Farm labour, small scale business , Sale of small livestock, piece wok, VSLs scheme
Garden Clearing, Tilling in lowland fields, mowing thatching grass, wedding initiation ceremonies, building houses. Sowing of vegetables and crops in lowland fields Harvest beans, maize, sorghum
Aug Sale of vegetables and other field crops; Farm labour; Sale of pottery; VSLs scheme; small scale business Harvest of cassava, artisanal dig minerals Sale of crops (maize, beans, Sale of small livestock (goats, sheep, hen, rabbits) piece wok, VSLs scheme
Same as July   Harvest of beans, maize
Sep Farm labour; Sale of pottery; VSLs scheme; small scale business Pay work in fields Sale of small livestock, piece wok, VSLs scheme
Same as July and land preparation (ridging) Season A: land clearing and preparation; sowing of crops; harvest of crops in lowland field Lan preparation for Season A; plant cassava; harvest cassava of previous year
Oct Farm labour; Sale of pottery; VSLs scheme; small scale business Selling harvest crops from marshland, pay work in fields Sale of small livestock, piece wok, SVLs Scheme
Ridging, planting of some crops especially maize, transplanting rain-fed tomato Weeding; harvest of crops in lowland field Prepare for Season A agriculture season; Sow beans and maize and cassava; harvest cassava of previous year
Nov Farm labour; Sale of pottery; VSLs scheme; small scale business Paid field work (weeding) Sale of small livestock, piece wok, VSLs scheme
Ridging, planting of some crops especially maize, transplanting rain-fed tomato Weeding; harvest of crops in lowland field Prepare for Season A agriculture season; Weed beans and maize; harvest cassava of previous year
Dec

Farm labour

Sale firewood; Share of VSLs scheme; small scale business

Sale of harvest produce Farm labour, Sale of small livestock, piece wok, VSLs scheme
Planting maize, tobacco, beans , soya, sweet potato, millet etc, basal application of fertilizer, weeding, transplanting rain-fed tomato Weeding and harvesting of short-cycled crops Harvest beans; to prepare next agriculture season
  1. Household Assets [E13]

Even though the evaluation of households assets is not part of the Logframe or included as programme indicators, this information has been captured through the questionnaire and therefore should be compared in following evaluations (Mid-term and final) to show the impact of programme activities – e.g. farming tools distribution, livestock distribution, saving and credit groups - on building productive assets at household level.

Malawi – CADECOM Dedza: Only 2% of surveyed households have savings in the bank or other formal financial institutions. However, 10% of households have reported being part of community-based saving schemes with an average amount of saving of 1,643MK ($2.4), while 31% keeps cash saving at home, 1,935MK (3$) on average.[E14]

In terms of farming inputs, only a negligible percentage of households reported to have any stock of maize seeds or fertiliser, 3% and 1% respectively. [E15]The average land ownership among respondent households is 1.85 acres (0.7 ha), however interestingly only 54% of households farm it entirely, while 30% farm at least half of it and 16% of households farm it some but less than half (Fig. 9) [E16]

Malawi – CADECOM Chikwawa: Only 1.5% of surveyed households have savings in the bank or other formal financial institutions. However, 21% of households have reported being part of community-based saving schemes with an average amount of saving of 3,276MK ($4.8), while 34% keeps cash saving at home, 2,945MK (4.3$) on average. In terms of farming inputs, 12% keep rice seeds, 9% keeps maize seeds and only 4% some chemical fertilisers. For details on the quantities kept see Annex 2. The average land ownership among respondent households is 1.16 acres (0.5 ha). 58% of households farm it entirely, while 33% farm at least half of it and 6% of households do not farm it at all (Fig. 10).  

DRC – CDJP Bukavu: only 1% of respondents save money in the bank with an average amount of 866cf ($0.93) while 15% of total households inquired keep savings within community saving and credit schemes, an average amount of 11,106cf ($12) and only 11% keep some cash at home, 3,621cf ($4) on average. In terms of farming inputs, 10% and 5% of respondent households keep some stock of bean and maize seeds respectively, while 27% of households keep few kilos of chemical fertiliser The average land ownership among respondent households is 2 ha, however only 47% cultivate it entirely, while 43% farm at least half of it, 6% farm some but less than half of it and 4% none (Fig.11)

Rwanda - CDJP Kigali: 11% of respondents save money in the bank with an average amount of 3,442rf ($4.4), 61% of total households inquired keep savings within community saving and credit schemes, an average amount of 9,465rf ($12) and 18% keep some cash at home, an amount of 3,644rf ($5) on average. In terms of farming inputs, 68% and 66% of respondent households keep some stock of bean and maize seeds respectively, while 50% of households have a stock of organic fertiliser in the form of manure. The average land ownership among respondent households is 14m X 14m (0.2 ha), however only 54% cultivate it entirely, 19% farm at least half of it, 24% farm some but less than half of it and 3% none (Fig. 12)

In terms of access to farming inputs such as seeds and fertilisers, most FGDs participants highlighted the challenges to access them, mainly as financial and logistic limitations. [E17]In Malawi people either recycle local seeds from previous harvest or purchase them at ADMARC (Agricultural Development and Marketing Cooperation), registered agro-dealers and vendors in the local market. Currently a relevant portion of seeds and fertilizer used for farming, come from government’s FISP (Farm Input Subsidy Program) program, although many lamented issues of great delay in delivery and cases of corruption. Large distances that people have to cover to buy inputs is one of the challenge.

In DRC farming inputs are also purchased in the local market or bought/provided by neighbours. It does not seem to be any public or private agency to provide farming inputs and these are therefore provided by local vendors mainly. Poor farmers seem to rely on local variety and organic fertilisers such as compost and manure, though their quantities are very limited. Overall, the main limitation to access inputs and tools remains the financial resources as some women reported having to go to bed hungry in order to buy a hoe and seeds. In selected villages in Rwanda, people often buy farming inputs at the nearest market when they need. There are no regular service providers to supply the inputs, and high prices is the main constraint to purchase fertilizers and hybrid seeds.

Figure 19 Relative land cultivation - DRC CDJP Bukavu

Malawi – CADECOM Dedza: 40.9% of respondent households own some sort of livestock, while 59.1% do not own any of them. [E18]The majority of households keeping any livestock, own chickens (34.4%), followed by pigs and goats (11% each) and only 3.9% own either cattle or oxen, while donkeys are owned by only 0.6% of respondent households (Fig. 13).

Malawi – CADECOM Chikwawa: 46% of total households own some type of livestock and 54% of households do not own any. The majority of households keeping any livestock, own chickens (41%), followed by goats (16% each) and pigs (6%), only 5% own cattle (Fig. 14).

DRC – CDJP Bukavu: the total share of households owning any livestock is 47% while 53% of households do not own any livestock. In terms of type of livestock, 23% of those owning any livestock keep chickens, 21% keep goats and 20% keep pigs, while only 8% keep cattle (Fig. 15).

Rwanda - CDJP Kigali: 65% of respondent households own some type of livestock while 35% do not own any. In terms of type of livestock, 43% of those owning any livestock keep goats, 22% keep chickens and 16% keep pigs, only 13% keep cattle (Fig. 16).

This information is helpful when partners will have to start livestock distribution as one of programme activities. It is foreseen that in year 2 half beneficiaries will receive livestock (types and amount still to be decided according to local prices and suitability). Since it emerged from the questionnaires that among target beneficiaries there are households owning some livestock and others who do not, partners should try to prioritise those without any livestock to start with.  

Figure 24 Livestock types owned by respondents HHs - RW CDJP Kigali

A list of assets commonly owned at household level was presented to respondents and asked if they owned any of them and if yes, how many. It should be noticed that kitchen utensils have purposely not been included in the list.

Malawi – CADECOM Dedza: the most owned asset is the farming hoe, as roughly 90% of households own at least one but the majority up to three, followed by iron sheet, radio, bicycle [E19]and mobile phone. [E20]The graph below (Fig. 17) reports the differences in ownership rate by asset. The graph showing households owning a car, motorbike and TV are a mistake in data collection or data entry   [E21]

Malawi – CADECOM Chikwawa

The most owned asset is the farming hoe, as roughly 83% of households own at two on average, followed by bicycle owned by 25% of respondent households, mobile phone 18%, radio 16%, iron sheet 11% and solar panel kept by 8% of households. The graph below (Fig. 18) reports the differences in ownership rate by asset. Surprisingly, two households were found having a car and 15 households having a TV. This has been verified with the partner and seem to be mistakes in the data collection or data entry.     

DRC – CDJP Bukavu: the most owned asset is the farming hoe, 83% of households owning up to three, followed by beer drum (13%), mobile phones (9%), radio (8%) and iron sheet (6%). The graph below (Fig. 19) reports the differences in ownership rate by asset.

Rwanda - CDJP Kigali: the most owned asset is the farming hoe, 88% of households owning up to three of them, followed by mobile phones (23%), radio (19%), bicycle (17%), beer drum (16%), iron sheet (11%) and solar panel (11%). The graph below (Fig. 20) reports the differences in ownership rate by asset.

  1. Food Security

On food security we aimed at exploring three main areas:

  1. Dietary diversity at household level by inquiring how often each of the food group is consumed on average in a week.[E22]
  2. Main sources of food, by asking respondent HHs from which among a list of different food sources (own production, gathered food; received from piecework; purchased on the market; etc.) they would obtain food from and in which rate (all, most, some, a little, none).[E23]
  3. The period of hunger gap or the number of months a household had experienced food insecurity during the last year. N.B. A month was defined food insecure if the household eat less than two meals per day for a continuous week. This is directly related to the Logframe Outcome Indicator 1.1

Malawi – CADECOM Dedza

Maize, mainly in the form of ‘nsima’ the national staple food, is consumed by 98% of households and 39% of them eat it on a daily basis. Vegetables are consumed by 96% of respondent households and eaten every day by 86% of them. They are followed by fruits 52% of HHs (mainly twice a week), vegetable oil 43% of HHs and pulses and beans 34% (mainly once or twice a week), etc. The diagram below (Fig. 29) shows the % of HHs consuming each food group at least once a week. For details on the frequency of consumption see Annex 1.[E24]

In terms of food sources the large majority of respondents mentioned four main food sources, namely food received from piecework mainly farm labour, gathered foods, own farm production, and food purchased at the market (Fig. 30). [E25]

Malawi – CADECOM Chikwawa

Maize, mainly in the form of ‘nsima’ the national staple food, is consumed by 92% of households on average 4.4 times a week. Sweet potato and cassava are also consumed by 33% and 14% of households respectively once a week overall. Vegetables are consumed by 84% of respondent households on average three times a week, followed by beans (33%) consumed once or twice a week, fish (33%), fruits (26%), vegetable oil consumed by 76% of HHs. The graph below (Fig. 31) reports the % of HHs consuming each food group at least once a week. For details on the frequency of consumption see Annex 2.

In terms of food sources there seem to be three main food sources: food received from farm labour, food purchased at the market and gathered foods, such as water lilies locally known as “nyika”, collected along the Shire River and swamps along Ndindi Mash. Interestingly own production has a very low relevance as a food source at household level (Fig. 32).

For what concerns the main sources of food, FGDs confirm this trend, only in Nsanje District (CADECOM Chikwawa) own production is reported as the main source of food while in the HHs questionnaire it comes out as more an equal balance between own production, piece work and gathered food (wild fruits/tubers like “nyika” water lilies). Own production will probably be the main food sources after the harvest period, combined with other sources such as farm labour and the market following that. In Malawi in fact often people engaged in farm labour are paid in kind with maize flour. Further analysis will be required to better understand this trend due to some discrepancy between results from the questionnaires and FGDs.

DRC – CDJP Bukavu

Cassava is the main staple in the form of ‘nsima’ consumed by 94% of households six times a week on average. Vegetables are consumed by 86% of respondent households and eaten 5 days a week on average. They are followed by sweet potato (41%), fish (34%), beans (30%) and fruits (25%) mostly consumed once a week. The graph below (Fig.33) shows the % of HHs consuming each food group at least once a week. For details on the frequency of consumption see Annex 3.

In terms of food sources two of them are the most commonly utilised, own farm production and purchase at the market. 60% of HHs reported to get all or most of their food from own production, and 12% some of it, 24% of HHs rely on the market for purchasing food (“all” and “most” of it), while 24% buy “some” of it from the market (Fig. 34). Other sources like food gathering and reception from piecework seem to be considered at much lower extent, for details see Annex 3.

Rwanda - CDJP Kigali

Maize is the main staple in the form porridge “boullie” or dough food “pâte” consumed by 87% of households three times a week on average, followed by sweet potato (64%) consumed twice a week and cassava (52%) eaten on average once a week. Vegetables (95%) and beans (93%) are also eaten by the large majority of households consumed six and four times a week respectively. Vegetable oil is consumed by the 66% of respondents on average three times a week. Figure 35 below reports the % of HHs consuming each food group at least once a week. For details on the frequency of consumption see Annex 4.

The main food source is the market with 45% of respondent households reporting to get “all” or “most” of their food by purchasing it. 27%of HHs relay on their own farm production for all or most of the food consumed, while 27% of HHs for “some” of their food. Gifts and loans is another substantial way of accessing food with 12% of HHs highly relying on this sources and 14% of HHs using it for “some” of the food (Fig 36). Other sources like food gathering and reception from piecework seem to be considered at a much lower extent, for details on % see Annex 4.

In DRC and Rwanda the FGDs have confirmed the information gathered through the HH questionnaires, which is food is mainly produced and on a less extent purchased in the market since farm labour here is mainly paid in cash, thus providing a source of income to buy food when own production lacks.    

We also inquired about the hunger gap period or the months in which the household experience food insecurity, defined as eating less than two meals per day for more than a continuous week within a month). This directly informs the Logframe indicator below.

Outcome Indicator 1.1 No or % of households stating to be food secure* for at least 10 months in the last year.                                                                                

Malawi – CADECOM Dedza

The average number of months respondent households reported to be food secure is 6. However it was found that only 3% of FHH and 7.5% of MHH stated to be food secure for 10 months or more (Outcome Indicator 1.1).

The graph below (Fig. 37) shows that October throughout March is the lean period or the months in which the majority of households reported a state of food insecurity and therefore not having enough food to eat. The average months of food insecurity is 5.8. By comparing the seasonal calendar it emerges that the non-lean months do actually match the harvest period when farmers start to consume their own production or sell the production to buy other foods. The food security situation has worsened recently due to the combination of the early 2015 flood and the 2015-2016 El Nino effect, and the same can apply for the Chikwawa District (FAO Crops Prospects and Food Situation, 2016). These results show a trend of people inability to either grow enough food or earning enough income to ensure all members of the households are able to eat at least two meals per day for the entire year.    

Malawi – CADECOM Chikwawa

The average number of months respondent households reported to be food secure is 7.5. In addition to that, it was found that 17% of FHH and 17% of MHH stated to be food secure for 10 months or more (Outcome Indicator 1.1).

DRC – CDJP Bukavu

the average number of months respondent households reported to be food secure is 7 (see Annex 3). However it was found that 19% of FHH and 39% of MHH stated to be food secure for 10 months or more (Outcome Indicator 1.1).

This yearly trend of food security follows the rainy and production seasons in DRC whereby the first harvest is obtained in January thus allowing HHs to have food to either eat or sell in order to buy other foods.

Rwanda - CDJP Kigali

The average number of months respondent households reported to be food secure is 8 (see Annex 4). It was found that 26% of FHH and 23% of MHH stated to be food secure for 10 months or more (Outcome Indicator 1.1).

This yearly dual trend of food security seems in line with the two rainy and production season A and B present in Rwanda. People start harvesting in December and then again in May, while the peaks of food insecurity from September to December and from March to May follows this path. In addition to that the harvest of the cassava towards the end of the year, contribute to food security through its consumption and sale in the market[E26].

  1. Farming practices [E27]

During the household surveys we asked what farming practices are considered the most valuable to improve production, along with asking what irrigation system do they mainly utilise. The results will provide insights into what is perceived useful for obtaining a good harvest by farmers in the area, what irrigation facilities are at their disposal, thus helping us to better design the agricultural and irrigation component and training needs for farmers.

Malawi – CADECOM Dedza

In Ntcheu District the farming practices considered more valuable to improve crop production are the application of chemical fertilisers mentioned by 73% of respondents followed by land preparation (66%), weeding (60%) and use of hybrid/improved seeds varieties (46%). The graph below (Fig. 41) show the various preferred options of farmers. In terms of irrigation system, the large majority of respondents (63%) mentioned watering cans followed by the use of treadle pump (14%). [E28]

Malawi – CADECOM Chikwawa

In Nsanje District, the farming practices considered most valuable are land preparation mentioned by 57%, followed by minimum tillage (50%), manure application (39%), and the weeding and the application of chemical fertiliser (both 35%). However also soil and water conservation practices and use of hybrid/improved seeds are relevant to farmers. The graph below (Fig. 42) show the various preferred options of farmers. In terms of irrigation system, only the 14% of respondents utilise any irrigation system and all of them use watering canes.  

DRC – CDJP Bukavu

In Mulamba District, the farming practices considered most valuable are land preparation mentioned by 81%, followed by manure application (65%), weeding (34%), minimum tillage (23%) and crop association (21%). The graph below (Fig. 43) show the various preferred options of farmers. In terms of irrigation system, only the 23% of respondents utilise any irrigation system and the large majority specifically utilise watering canes.  

Rwanda - CDJP Kigali: the farming practices considered most valuable in targeted villages of Bugesera District of Rwanda are land preparation mentioned by 91%, followed by weeding (76%), manure application (66%) and crop rotation (62%). The graph below (Fig. 44) show the various preferred options of farmers. In terms of irrigation system, only the 26% of respondents utilise any irrigation system which is predominantly watering canes.    

  1. Climate Change

Across all countries, most FGDs participants seemed to be familiar with the concept of climate changes described by most as erratic rainfall, more frequent floods and storms, raise in temperatures, drying up of rivers and plant diseases. [E29]The impact is prolonged dry spells that lower crop production, damages on housing and other building and at times destroying crops and trees. Usually rainfall stops early before the crops mature and there is little water for irrigation.

When participants were asked if they thought everybody in the community are affected by CC in the same way, some acknowledged that the most vulnerable and certain categories (elderly and orphans) are also those mostly affected because they lack diversified sources of income and mainly rely on farming for subsistence, they are not able to adequately store their crops and lack means to control pests and diseases.

Some coping strategies mentioned in the FGD are renting farmlands along the river for winter cropping and irrigation farming, practising conservation agriculture, heavier dependence on piece work, use of drought resistance varieties, trees plantation to reduce soil erosion and income diversification through the establishment of saving and credit groups. In Rwanda food banks at village level have been suggested in order to cope with climate changes. Each resident should bring a stock or a certain quantity of its production that will help when the famine occurs and to obtain seeds to be used in the next agricultural season. Residents should then repay agricultural inputs received through communal work by digging ditches that will help to collect rainwater to be used for the irrigation of vegetables.

In the Community Household Survey we have enquired about the impact of climate change asking respondents to mention up to four main climate change hazards currently impacting their farming activities, along with mentioning any coping mechanisms they are aware of to mitigate the impact of climate change. Results show a good level of awareness about climate change but somehow lower levels of specific coping strategies. It should be noticed that figures of knowledge of climate change in Rwanda are very high compared to those found in other countries. This can be explained either by the presence of government’s programmes to raise awareness on climate change, or by some data quality issue during data collection or data entry. This figure will need to be closely monitored in follow-up reviews.  

Outcome Indicator 1.4 (OI): % of community members able to mention at least 3 major climate related risks which are affecting them and know how to mitigate them by mentioning at least one coping strategy related to each risk.

Malawi – CADECOM Dedza: see below main climate change risks, from the most commonly mentioned by respondent households to the least one:

-         74% mentioned “Erratic rainfall”

-         71% mentioned “Increase in temperatures”

-         33% mentioned “Crops unable to reach maturity due to extreme weather conditions”

-         23% mentioned “Change of weather patterns”

-         19% mentioned “Complete crop failure due to extreme weather events”

-         18% mentioned “Early drying up of streams and other water sources”

-         7% mentioned “Extreme weather events”

-         7% mentioned “Increase in pests and diseases in crops”

-         4% mentioned “Occurrence of new pests and diseases”

18% of female respondents and 13% of male respondents mentioned at least three [E30]climate change risks AND for each of these mention at least one (matching) coping strategy (Outcome Indicator 4)

In the Focus Group Discussion [E31]it was reiterated that almost everybody in the community now know what climate change is about, experienced as rise in temperatures, frequent droughts and prolonged dry spells, heavy storms damaging houses, rivers drying up early all leading to lower crop production, poverty and malnutrition. FGD participants explained that such lower harvest do not meet their annual food requirement thus becoming perpetually dependent on piecework, and the importance of piecemeal work emerges also from the discussion on annual income. This means that fields are not attended to and this trap them in a cycle of food insecurity.

Irrigation, the use of drought resistant varieties and income diversification were mentioned as main coping strategies to climate change, and on this regard saving and credit group are considered very helpful to raise small capitals for investing in income generating activities.

Malawi – CADECOM Chikwawa: see below main climate change risks mentioned by respondent households:

-         69% mentioned “Erratic rainfall”

-         56% mentioned “Increase in temperatures”

-         49% mentioned “Crops unable to reach maturity due to extreme weather conditions”

-         10% mentioned “Change of weather patterns”

-         53% mentioned “Complete crop failure due to extreme weather events”

-         32% mentioned “Early drying up of streams and other water sources”

-         14 % mentioned “Extreme weather events”

-         3% mentioned “Increase in pests and diseases in crops”

-         6% mentioned “Occurrence of new pests and diseases”

10% of female respondents and 7% of male respondents mentioned at least three climate change risks AND for each of these mention at least one (matching) coping strategy (Outcome Indicator 4)

DRC – CDJP Bukavu:

In DRC, where climate change is just one among several other risks including insecurity and civil conflicts, participants reported that in the past years rainy season used to be of 9 months and the dry one of 3 months, while currently the trend has shifted to 7-8 and 4-5 months respectively.

See below main climate change risks mentioned by respondent households:

-         21% mentioned “Erratic rainfall”

-         17% mentioned “Increase in temperatures”

-         37% mentioned “Crops unable to reach maturity due to extreme weather conditions”

-         33% mentioned “Change of weather patterns”

-         10% mentioned “Complete crop failure due to extreme weather events”

-         3 % mentioned “Extreme weather events”

-         44% mentioned “Increase in pests and diseases in crops”

-         23% mentioned “Occurrence of new pests and diseases”

7% of female respondents and 14% of male respondents mentioned at least three climate change risks AND for each of these mention at least one (matching) coping strategy (Outcome Indicator 4)

Rwanda - CDJP Kigali:

An impact of CC mentioned in Rwanda is the migration of some residents to other regions to look for employment, inability to pay the health insurance and malnutrition.

See below main climate change risks mentioned by respondent households.

-         90% mentioned “Complete crop failure due to extreme weather events”

-         74% mentioned “Increase in temperatures”

-         71% mentioned “Erratic rainfall”

-         43% mentioned “Crops unable to reach maturity due to extreme weather conditions”

-         7% mentioned “Change of weather patterns”

-         2% mentioned “Extreme weather events”

-         70% mentioned “Increase in pests and diseases in crops”

-         30% mentioned “Occurrence of new pests and diseases”

92% of female respondents and 95% of male respondents mentioned at least three climate change risks AND for each of these mention at least one (matching) coping strategy (Outcome Indicator 4).

  1. Gender Analysis

In the Community Household Survey we inquired about three areas around gender:

  1. Gender roles
  2. Sexual and Gender Based Violence
  3. Women Participation and leadership
  4. Decision-Making & Control
  1. Gender Roles

In the FGDs, carried out separately with women and men groups, both participants mentioned that women’s main chores in the household are looking for food and preparing it, fetching water and firewood, taking care of children, cleaning and laundry, collecting fodder for the livestock, in addition to the work in the field. This emerged across all countries and do not seem differ much between dry and rainy season. Only the agricultural activities intensify during the production period, thus making women busier. Both men and women, in separate groups agreed that tasks in the households are not equitably distributed and that women have more tasks and responsibilities than men in the household.

On the reasons for such unequal distribution, some women mentioned that certain tasks have been always associated with women, men are the heads of the households and for this reason sometimes they tend to treat women as heavy labourer. Men instead mentioned cultural reasons as well as “women being used to heavy work since their creation”. Women lamented this as an unfair conditions and called for a fairer distribution of work that will see both men and women sharing all the chores within the household. Men in Malawi mentioned that they could be available in a fairer share of the household’s tasks in order to release women, such as chopping the firewood, drawing water and going to the maize mill. Also, in Rwanda men said that they were comfortable to take responsibility of certain tasks when their spouses are not available or sick, whereas in DRC seemingly local culture and men’ attitudes prevent them to show a certain disposition to release women from certain tasks.      

During the Community Household Survey, we made a series of statements and asked to both women and men respondents whether they agreed or not, to better understand perceptions of gender roles. Here below the statements made:

  1. Household chores are the sole responsibility of the women and children in the household.
  2. Women are generally not very good at supervising, so they should not be responsible for organizing community work.
  3. For community volunteer work, it is better to send women so that the men can focus on more important/productive activities
  4. If the husband and wife both have fields to plow, it is better to plow the man's first because it is more important
  5. If a woman becomes too powerful, she won't respect her husband and this will cause problems for the household

For results on all statements see Annex 6, 7, 8 and 9. For this baseline report we decided to focus on two meaningful statements, namely n. 1 and 2 highlighted in red and results are reported below for each country. Overall, most of both men and women respondents do not agree with the fact that “Women are generally not very good at supervising, so they should not be responsible for organizing community work”, while the other statements is more controversial.

Malawi – CADECOM Dedza:

In Dedza (Ntcheu District), 45% of men agree with the fact that households chores are the onl responsibility of women and children and 17% of women respondents also agree with it. This shows strong cultural barriers  

Malawi – CADECOM Chikwawa

In Chikwawa and Nsanje District the large majority of both men and women do not agree with this statement and likely they believe that households chores should be more equally shared, as it emerged from the FGDs.

DRC – CDJP Bukavu

In DRC the majority of both men and women do not seem to agree to that household chores are the sole responsibility of women and children. However, this seems in contrast with results from the FGD where men seemed less open to a change of attitudes towards helping women with household tasks. There is a possibility that in answering such questions during the survey, men would reply the “most acceptable” answer.  

Rwanda - CDJP Kigali:

In Rwanda 40% of men believe that household chores are the primary responsibility of women and children, whereas the majority of women do not agree with it and would probably like to see a more equal share of tasks in the household, as reported from the FGDs.    

  1. Sexual and Gender Based Violence

On sexual and gender based violence (SGBV) we asked if respondents heard about any incidences of cases of SGBV in the last year or so, and if yes how many. The cases were from a range of examples such as rape, defilement, domestic violence, forced marriage, etc. (see tables below for the full list). The tables below show the average of reported incidences among respondents. For our indicator on SGBV we have selected cases of “wives being beaten by their husbands” and we will report consistently about this number of cases.  

In addition to that the interviewers also made statements about domestic violence and asked respondents whether they found acceptable for a husband to beat their wife in certain circumstances in order to better understand perceptions on SGBV. We have selected the following statement which will measured throughout the programme:

  1. 1.In your opinion, is it acceptable for a husband to beat his wife if she goes somewhere without his approval?

Output Indicator 3.3: Reduction in incidence of sexual and gender based violence among targeted farming householdswas measured in two ways, 1) yearly average reported cases of wives being beaten by their husbands, and 2) % of men AND women answering that is SOMETIMES acceptable for a husband to beat his wife if she goes somewhere without his approval.

Overall the number of cases and incidences of SGBV reported by respondents are negligible, thus showing either a very low level of SGBV or under reporting. Under reporting might be the most plausible explanations since triangulating these with results of the FGDs, both men and women remarked that SGBV is an issue for women and the whole community. Most common incidences are wife beating, forced sex, lack of control of household wealth by women, denial to rights to association by men, family conflicts, marital rape and harassment. This in the end cause psychological and physical defects in women “leading to depression, loss of concentration on households’ work hence retarding the household’s development” (quote from a FGD participant in DRC).

Below the figures reported for Output Indicator 3.3 on cases of SGBV reported along with the SGBV attitudes by country.

Malawi – CADECOM Dedza:

In Focus Group Discussion in Ntcheu, participants said that the community faces a lot of SGBV. The most notable SGBVs cases include wife battering, forced sex, lack of control of household wealth by women, women been denied rights to association by men. These cases lead often to both psychological and physiological suffering in women which impact household development.

Despite this results from the FGDs, in the Community Household Survey yearly average number of cases of a wife being beaten by her husband reported is 0.95. Such a discrepancy between the FGDs and the results of the household questionnaires, shows that a different approach to report SGBV cases in the community is needed. On the second sub-indicator, 6% of men and 7% of women stated that is SOMETIMES “acceptable for a husband to beat his wife if she goes somewhere without his approval”.

Table 9 Average cases of SGBV reported in Malawi Ntcheu district

Sexual and Gender Based Violence I
  N Minimum Maximum Mean Std. Deviation
In the last month, how many incidences of a wife being beaten have you seen or heard about? 115 0 6 .95 1.330
In the last month, how many incidences of a husband being beaten have you seen or heard? 115 0 5 .30 .827
In the last year, how many incidences of forced marriage have you seen or heard about? 115 0 6 .70 1.344
In the last year, how many incidences of defilement (below age 16) have you seen or heard about? 115 0 8 .52 1.202
In the last year, how many incidences of rape have you seen or heard about? 115 0 6 .17 .704
In the last year, how many incidents of sexual exploitation have you seen or heard about? 115 0 6 .45 .910
In the last year, how many cases have you heard being reported to community policing or victim support unit? 115 0 5 .51 .902
In the last year, how many cases of a wife being beaten have been resolved by the extended family, church representatives or someone else in the village? 115 0 5 .70 1.155
In the last year, how many cases of forced marriage have been resolved by the extended family, church representatives or someone else in the village? 115 0 3 .23 .676
In the last year, how many cases of defilement (below age 16) have been resolved by the extended family, church representatives or someone else in the village? 115 0 9 .21 .922
In the last year, how many cases of rape have been resolved by the extended family, church representatives or someone else in the village? 115 0 4 .17 .648
Valid N (listwise) 115        

Malawi – CADECOM Chikwawa

In Nsanje, FGDs results are very similar to those in Ntcheu District, namely a high incidence of SGBV in the community, showing again a discrepancy with the results emerging from the HHs questionnaire. In fact here, the yearly average of cases of a wife being beaten by her husband is 0.54, which shows a very low incidence. For the second sub-indicator, 7% of men and 7% of women stated that is SOMETIMES “acceptable for a husband to beat his wife if she goes somewhere without his approval”.

Table 10 Average cases of SGBV reported in Nsanje district

Sexual and Gender Based Violence I
  N Minimum Maximum Mean Std. Deviation
In the last month, how many incidences of a wife being beaten have you seen or heard about? 72 0 5 .54 1.162
In the last month, how many incidences of a husband being beaten have you seen or heard? 72 0 4 .17 .628
In the last year, how many incidences of forced marriage have you seen or heard about? 72 0 5 .43 1.111
In the last year, how many incidences of defilement (below age 16) have you seen or heard about? 72 0 2 .04 .262
In the last year, how many incidences of rape have you seen or heard about? 72 0 1 .01 .118
In the last year, how many incidents of sexual exploitation have you seen or heard about? 72 0 10 .24 1.316
In the last year, how many cases have you heard being reported to community policing or victim support unit? 72 0 5 .22 .755
In the last year, how many cases of a wife being beaten have been resolved by the extended family, church representatives or someone else in the village? 72 0 5 .35 .952
In the last year, how many cases of forced marriage have been resolved by the extended family, church representatives or someone else in the village? 72 0 5 .14 .657
In the last year, how many cases of defilement (below age 16) have been resolved by the extended family, church representatives or someone else in the village? 72 0 1 .03 .165
In the last year, how many cases of rape have been resolved by the extended family, church representatives or someone else in the village? 72 0 1 .01 .118
Valid N (listwise) 72        

DRC – CDJP Bukavu

In DRC women participants to the FGDs have reported the following routine types of violence against women:

  • Discrimination against baby girls: for some men, if a woman has not given birth to a boy, they say that it does not have children
  • Verbal harassment against women
  • Men spend spending household assets in an uncontrollable way
  • Women/girls cannot inherit the property of his parents
  • Inability to complain or bring a man to justice.
  • Women and children being abandoned by their husbands leaving ten children.

FGDs explained that the victims of SGBV in DRC are discriminated in the community and this frustration sometimes lead to psychological illnesses. These women are discriminated against and this hinders their development. Others have shown that women victims of violence are abandoned and live in desolation and total despair

In the household questionnaire conducted in DRC Mulamba district of South Kivu, the yearly average of cases of a wife being beaten by her husband is 0.37. For the second sub-indicator it was found that 29% of men and 45% of women stated that is SOMETIMES “acceptable for a husband to beat his wife if she goes somewhere without his approval”. It is interesting to notice that proportionally more women than men believe that it is acceptable for men to beat their wives. 

Table 11 Average cases of SGBV reported in Mulamba district

Sexual and Gender Based Violence I
  N Minimum Maximum Mean Std. Deviation
In the last month, how many incidences of a wife being beaten have you seen or heard about? 70 0 5 .37 .887
In the last month, how many incidences of a husband being beaten have you seen or heard? 70 0 4 .21 .740
In the last year, how many incidences of forced marriage have you seen or heard about? 70 0 3 .33 .696
In the last year, how many incidences of defilement (below age 16) have you seen or heard about? 70 0 5 .20 .734
In the last year, how many incidences of rape have you seen or heard about? 70 0 5 .23 .887
In the last year, how many incidents of sexual exploitation have you seen or heard about? 70 0 4 .19 .767
In the last year, how many cases have you heard being reported to community policing or victim support unit? 70 0 2 .13 .414
In the last year, how many cases of a wife being beaten have been resolved by the extended family, church representatives or someone else in the village? 70 0 4 .19 .644
In the last year, how many cases of forced marriage have been resolved by the extended family, church representatives or someone else in the village? 70 0 1 .03 .168
In the last year, how many cases of defilement (below age 16) have been resolved by the extended family, church representatives or someone else in the village? 69 0 4 .13 .567
In the last year, how many cases of rape have been resolved by the extended family, church representatives or someone else in the village? 70 0 2 .06 .336
Valid N (listwise) 69        

 Rwanda - CDJP Kigali

Also in Rwanda Bugesera district, FGDs participants mentioned violence against women based on domestic fighting, marital rape and family desertion, lack of right of free expression and of heritage. The impact is more destitution in the family, lack of collaboration between family members, poor living conditions of children, divorce, trauma and loneliness.

The yearly average of cases of a wife being beaten by her husband is 1.72. On the second sub-indicator 18% of men and 21% of women stated that is SOMETIMES “acceptable for a husband to beat his wife if she goes somewhere without his approval”.

Table 12 Average cases of SGBV reported in Bugesera Rwanda

Sexual and Gender Based Violence I
  N Minimum Maximum Mean Std. Deviation
In the last month, how many incidences of a wife being beaten have you seen or heard about? 60 0 5 1.72 1.637
In the last month, how many incidences of a husband being beaten have you seen or heard? 60 0 5 .37 .843
In the last year, how many incidences of forced marriage have you seen or heard about? 60 0 2 .07 .312
In the last year, how many incidences of defilement (below age 16) have you seen or heard about? 60 0 3 .20 .632
In the last year, how many incidences of rape have you seen or heard about? 60 0 30 2.52 4.785
In the last year, how many incidents of sexual exploitation have you seen or heard about? 60 0 7 1.37 1.717
In the last year, how many cases have you heard being reported to community policing or victim support unit? 60 0 10 .92 1.565
In the last year, how many cases of a wife being beaten have been resolved by the extended family, church representatives or someone else in the village? 60 0 5 .97 1.288
In the last year, how many cases of forced marriage have been resolved by the extended family, church representatives or someone else in the village? 60 0 1 .02 .129
In the last year, how many cases of defilement (below age 16) have been resolved by the extended family, church representatives or someone else in the village? 60 0 3 .08 .424
In the last year, how many cases of rape have been resolved by the extended family, church representatives or someone else in the village? 60 0 10 .70 1.629
Valid N (listwise) 60        

In Rwanda there are programmes to reduce cases of SGBV, such as the “evening women’s meeting”“umugorobaw’ababyeyi” at village level where women talk about their experiences of SGBV and try to find solutions. In FGDs participants agreed that through mobilization, various training, educating men and women on gender, promote joint decisions between men and women, communication and collaboration, and dialogue at home, SGBV can be reduced. In Malawi the need for linking the community to proper reporting structures which can assist with cases of SGBV was highlighted during the FGDs.

  1. Women Participation and leadership

In FGDs conducted in Malawi, there was a certain acknowledgment about women having the capability of being good leaders because by nature they are honest, patient, fair, listen attentively, and make decisions carefully, and that there is need to support them. Some women participate in community organisations and associations and some hold different leadership positions in the groups. On the contrary in DRC, several men said women cannot be good leaders because women may not know the interests of the community, they often lack confidence, they are not always available, have more responsibilities in the household causing the delay in the service, they are not flexible and often are not discreet. On the other side women reported that sometimes when they try to provide advices these are dismissed on the basis of their gender.

During the Community Household Survey we have asked to female respondents whether they belonged to any community-based committees or associations and how much active they considered their participation to them.

Output Indicator 3.2b in the Logframe report on % of target women farmers in leadership positions.  

During the baseline study, this information was gathered among randomly selected women in target villages. In future reviews it is recommended to gather this information among selected women beneficiaries to abide to the indicator (“% of target women…..”).

This information is provided below at country and partner level.

Malawi – CADECOM Dedza:

Only 3% of respondents are members of the Village development Committee (VDC), Farmer Associations and School Committee while 9% participate to the Civil Protection Committees. These are committees recently established by the government at community level to better prepare communities to the impact of climate change. More than half of them are members of the steering committees. When they were asked how they would describe their participation in meetings, 4 out of 5 answered “I always say what I think” and only 1 “Sometimes I say what I think” (Fig. 53). For more details see Annex 6. [E32]

Overall it was found that 7% of women respondents belonging to any community associations and committees, are in leadership position (members of the steering committee) (Output Indicator 3.2).

Malawi – CADECOM Chikwawa

12% of female respondent are members of the VDC but 43% of them described their participation as “I’m there mainly to listen”; 7% are members of a Farmers Association, 5% of the School Committee and 19% of respondents belong to the Civil Protection Committee and 50% of them described their participation as “Sometimes I say what I think” and 25% “I’m there mainly to listen” (Fig. 54). For more details see Annex 7.

Overall it was found that 9% of women respondents belonging to any community associations and committees, are in leadership position (members of the steering committee) (Output Indicator 3.2).

DRC – CDJP Bukavu

9% of respondents are members of the Village Development Committee most as simple members. Half of them described their participation as “I always say what I think” and the other half “Sometimes I say what I think”. For more details see Annex 8.

Overall it was found that 2% of women respondents belonging to any community associations and committees, are in leadership position (members of the steering committee) (Output Indicator 3.2).

Rwanda - CDJP Kigali:

29% of female respondents belong to the Village development Committees and 40% of them are members of the steering committees; 30% said they “Always say what they think” and 70% of them “Sometimes say what they think” during the meetings. 34% respondents are members of a farmers’ association and 43% are members of the natural resources committees. For more details see Annex 10.

Overall it was found that 20% of women respondents belonging to any community associations and committees, are in leadership position (members of the steering committee) (Output Indicator 3.2).

  1. Decision-Making & Control

In both men and women FGDs in all countries emerged that men are those who mostly take decisions on how money is used in the household, how land is used, how labour is used and actually exercise control over all household assets. Women seem to have control only over kitchen utensils within the household. In DRC FGDs women stated that they “cannot sell the assets of the household if there is an emergency that requires financial means in the household”. In Rwanda however, participants reported that the situations is slightly changing as today, both men and women talk to each other before taking decisions and that men and women interact more than in the past for taking decisions.

Women reported that this arrangement is not fine for them because some of the decisions that men make are often not in the interest of the family and they impact negatively on the wellbeing of the household. For example, some men sell farm produce for their own benefit such as using money to buy beer and gifts to other women.

In the Community HHs Survey (From section G. Decision-Making & Control of Community HHs Survey) we have asked to women not head of the households to tell us who takes decision in the household for various circumstances and who control resources within the household. The circumstances and questions follow below:

  1. For cash income earned specifically from you, who decides how to use the money?
  2. For any cash income earned by the household, who decides how to use the money for buying food?
  3. For any cash income earned by the household, who decides how to use the money for a medical expense for you?
  4. For any cash income earned by the household, who decides how to use the money for a medical expense for a child?
  5. For any cash income earned by the household, who decides how to use the money to pay for school fees?
  6. For crops that are produced by you for which some of the harvest has been stored, who decides when to sell the stored crop?
  7. For crops that are produced by you, who decides what to do with the harvest?
  8. For a small livestock to be sold for any reason, who decides if and when to sell it?

We selected the two statements highlighted in red on decision making over income and assets AND reported % women respondent (not head of the HH) answering “Usually me” and “Together.”, representing our sub-indicators for the Outcome Indicator 1.3. In future reviews there might be changes in the way we assess this aspect. All results can be found in Annex 6, 7, 8 and 9.

Outcome Indicator 1.3: % of women who have increased access to, control over, and ownership of assets and income.

Results from the household questionnaires seem slightly different if compared with the qualitative information gathered during FGDs. Here it emerges, of course with differences among countries, that women do seem to have a certain decision making power, mainly on the money earned by themselves, and whereas on assets like livestock, their control seems more constrained. Community HHs Survey were supposed to be conducted separately between men and women, thus women were interviewed alone in order for them to feel freer when answering sensitive questions. Although this was largely respected there might be instances in which this was not possible and answers might have been biased, thus justifying the discrepancy between households questionnaires and the FGDs. In future reviews this is an aspect that should be carefully considered and more emphasis will be given to ensure that interviews are conducted confidentially.  

Results for statements 1 and 8 are reported below and included in the programme Logframe:

Malawi – CADECOM Dedza:

  1. 1.For cash income earned specifically from you, who decides how to use the money?

26% of women respondent answered “Usually Me”

44% of women respondent answered “Together”

  1. 2.For a small livestock to be sold for any reason, who decides if and when to sell it?

4% of women respondent answered “Usually Me”

39% of women respondent answered “Together”


Malawi – CADECOM Chikwawa

  1. 1.For cash income earned specifically from you, who decides how to use the money?

7% of women respondent answered “Usually Me”

21% of women respondent answered “Together”

  1. 2.For a small livestock to be sold for any reason, who decides if and when to sell it?

7% of women respondent answered “Usually Me”

7% of women respondent answered “Together”[E33]

DRC – CDJP Bukavu

  1. 1.For cash income earned specifically from you, who decides how to use the money?

24% of women respondent answered “Usually Me”

50% of women respondent answered “Together”

  1. 2.For a small livestock to be sold for any reason, who decides if and when to sell it?

11% of women respondent answered “Usually Me”

42% of women respondent answered “Together”

Rwanda - CDJP Kigali:

  1. 1.For cash income earned specifically from you, who decides how to use the money?

0% of women respondent answered “Usually Me”

76% of women respondent answered “Together”

  1. 2.For a small livestock to be sold for any reason, who decides if and when to sell it?

4% of women respondent answered “Usually Me”

67% of women respondent answered “Together”

5          Conclusions & Recommendations

This baseline study and gender analysis provided the opportunity to investigate key areas of the programme at country level. The geographical and gender disaggregation was deemed necessary in order to have a thorough understanding of the differences in different contexts and between male-headed and female-headed households on income sources, annual income, food security, as well as differences between men and women regarding knowledge on climate change, related risks and coping mechanisms.

The whole exercise was useful to test SCIAF and partners’ capacities to conduct such a review. Few issues have emerged in terms of data quality and reliability on some of the information collected, like some discrepancies found between FGDs and surveys results around decision making and control of assets, which will require careful thoughts during future reviews. Some tools utilised to measure indicators such as the reduction in SGBV, were found inadequate and will need to be amended in further assessments. Partners will require further training and capacity building on the tools and the techniques to gather information and better probe respondents’ answers, especially the trickiest ones, like household income, food security and gender. Another aspect that might need some improvements is the analysis of the focus group discussions conducted in the villages. It sometimes lacked the depth and thoroughness and the interpretation of the gathered information, thus not fully exploiting the great potential that this technique may provide. A capacity building in PRA will fill this gaps.

Despite some weaknesses this exercise has been very helpful to better understand countries contexts in which the UKAM programme will operate, along with SCIAF’s and partners’ capacities and constraints to conduct similar reviews. Some recommendations have been provided in this report, while others will be addressed by continuing working hand in hand with the implementing partners throughout the programme implementation.                  

 

6          UKAM2 Logframe Indicators and Targets

Done by Editor


 [E1]Are these for both surveys (Target and Community) because the demographics are very different for each survey e.g. there are far more FHH in the targeted survey

CB: It refers to Target HHs Survey – so it’s representative of beneficiaries households rather than the overall population. I’ve clarified this in the text.

 [E2]All of these results need to be disaggregated – this must be done not just for the indicators

CB: followed up by the team

 [E3]Add in the other countries. Again this can be used to look at food security – are these crops which are eaten or out they cash crops – look at the figures to try and pull something out – which was more common to produce – which were more profitable

CB: This information come from the FGDs but we don’t have many details, so I doubt we can add all infos you suggested.

 [E4]Break this down in a more meaningful way – lets first mention the % of the main sources of income and then compare

CB: done it

 [E5]This needs to speak to the survey stats it seems to but it is useful to use the figures to back it up

CB: agree and I changed the phrasing, etc.

 [E6]Use the survey data to demonstrate this – is it that those cultivating more land have less of a reliance on piecework

CB: this correlation was not done but can be followed up by the team

 [E7]Lets have some stats to back this up

CB: this is more a qualitative information emerging from the FGDs in Malawi but I’ve added reference from the IHHSurvey 2012 in Malawi.

 [E8]Did this come from FGDs?

CB: yes

 [E9]Check this against the actual data

CB: Ok

 [CB10]I think first HHs do not produce enough to feed themselves, and because they also need cash to buy HH items and pay school fees, they need to sell some of their farming produce. But the amount sold is not enough, so they need to engage in farm labour to fill both needs, food and other items/services (school, health, etc.)  

 [E11]Add in figures about average income from crop sources

CB: this info is available and this will be followed up by the team

 [E12]I am not really sure what this adds – could we summarise it to bring out issues such as food security?

 [E13]There is no indicator about household assets – apart from the one related to women – which this doesn’t address. We need make it clear why this is included in the baseline and what it tells us about the programme. I wouldn’t put it at the beginning – start with income as the is the impact indicator

CB: ok, I think even if there are no specific indicators on assets we can still compare results at mid-term or end of the programme to show the impact - I’ve clarified this and moved this part after the income section.

 [E14]What does this show us – the programme plans to set up savings and loans groups – so this is useful info – what analysis can you add?

CB: same as above, even though this information is not included as indicator, it can be compared overtime to corroborate the impact of the programme.

 [E15]Again so what does this mean? The programme plans to work with seed banks – so this should be linked here – it is an indicator on membership so we can reference this.

CB: same as above  

 [E16]I think we need to compare this to other thing e.g. food security. Are people with more land more likely to be food secure – if not why not. What is the difference between female and male

CB: followed up by the team

 [E17]This needs to go with the household assets section to make sure it is cross-referenced with the survey data

CB: yes, moved

 [E18]So does this affect the plan to do livestock training – will you target those without livestock?

CB: good idea – I’ve added this as a recommendation for partners they should prioritise those who have no livestock at all first

 [E19]What does this tell us. Is this high/low how does this mean they could adapt in times of emergency.

CB: I guess this is quite hard to establish but again we can try to compare this with following evaluations

 [E20]As you are planning digital data this could be useful information

 [E21]The data needs to be cleaned and the chart re-done this should not be include if this if it is a mistake.

CB: you’re right but unfortunately the data analysis prepared the chart before knowing he needed to take out some items.    

 [E22]I think we need to give this a score – as this is a key measure in food security – different diversity measures exist

CB: this might be added as indicator in the new RF as an intermediary outcome – to follow up on this

 [E23]This surely needs to be linked to income – I mean if their food security is high as they make enough from the crops then maybe their income is lower

CB: I’m not totally sure if we can see a difference on this. In rural areas everybody engage in agriculture to produce food, but very poor families with little land and few resources produce little, so they sell also little and engage in farm labour to fill the gap, resulting in very low diversity score and food security.

 [E24]This needs to be more meaningful – I think we need to know how many households are having diversity – this isn’t clear

CB: data is available and the team can follow up on this to clarify.

 [E25]Again clarify this – what is useful is per household – or something clearer so we can see across households how many sources do they get food from

CB: okay agree, but this requires another analysis and the team can follow up on this. However this already tells us that piecework seems to be the main source of food (in Malawi often people are paid in kind for farm labour), whereas own food production seems to be a less relevant source of food. However I agree we might get clearer information analysis “by household” instead of “per food source”  

 [E26]This needs more unpacking – linking to the diet diversity and the seasonal chart

 [E27]This section has no analysis – so what – what does this mean for programming –

CB: which farming practices are considered most valuable (as a starting point) and provided results. However, in the new RF we can inquire about knowledge of framing practices and actual adoption.

 [E28]Disaggregate by FHH/MHH

CB: agree and will be followed up by the team

 [E29]Where- all countries?

CB: yes across all countries

 [E30]We need to get more analysis from this – what three did they more commonly know – was this different between FHH and MHH

CB: I mentioned that risks are listed from the most common to the least.

Gender disaggregated analysis can followed up by the team

 [E31]In each of them – can we try and drag out some quotes.

CB: we have no quotes from FGDs, something we should address in future FGDs

 [E32]It is difficult to assess whether these are low levels without information on the size if the committee and its gender distribution

CB: you are right but I doubt we currently have this information. This is something we should consider in future review  

 [E33]Dramatically different to the others – why?

CB: not sure why


Date de publication : le 23 avril 2017 à 10:51:00,
Publié par : Administrator Caritas-CDJP

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