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Zim

babwe Resilience

Res earch Report Febru ary 1 2018

Prepared for: Center for Resilience (C4R), USAID Mission Zimbabwe

Recommended Citation:

TANGO International. 2018. Zimbabwe Resilience Research Report. Produced as part of the

Resilience Evaluation, Analysis and Learning (REAL) Associate Award. Photo Credit: Colin Crowley/Save the Children

Disclaimer: This report is made possible by the Resilience Monitoring, Evaluation, Assessment, Strategic

Analysis and Capacity Building Associate Award (The REAL Award). The REAL Award is made

possible by the generous support and contribution of the American people through the United

States Agency for International Development (USAID). The contents of the materials

produced through the REAL Award do not necessarily reflect the views of USAID or the

United States Government.

Prepared by:

TANGO International, Inc.

376 South Stone Avenue

Tucson, Arizona 85701 USA

Photo Credit: Colin Crowley/Save the Children

Zimbabwe Resilience Research Report

ACKNOWLEDGEMENTS iii

Acknowledgements

We would first and foremost like to thank the Center for Resilience and USAID/Zimbabwe for the

opportunity to conduct this study and their invaluable guidance over the course of its

implementation. In particular, Tiffany Griffin, Jason Taylor and Justin Mupeyiwa who responded to

questions, clarified information, and provided insightful comments.

We would also like to express our appreciation to George Kembo, the Zimbabwe Food and

Nutrition Council (FNC), and all others who attended the USAID-sponsored Resilience

Measurement Workshop in Harare, in August 2017, and contributed important suggestions for

improving the analysis and presentation of study findings. These contributions immensely improved

the quality of this report.

Finally, we would like to acknowledge the Zimbabwe Vulnerability Assessment Committee

(ZimVAC) who implemented the four household surveys and two community surveys. Without

their high-quality data collection, data cleaning, and analysis, this study would not have been

possible.

Resilience Evaluation, Analysis and Learning (REAL)

CONTENTS iv

Table of Contents

Acknowledgements ............................................................................................................... iii

Table of Tables ......................................................................................................................... v

Table of Figures ...................................................................................................................... vii

Acronyms ......................................................................................................................... viii

Executive Summary ............................................................................................................... ix

1. Introduction ....................................................................................................................... 1

2. Methodology ...................................................................................................................... 6

Data sources ............................................................................................................................................... 6

Factor analysis – household resilience capacity index ....................................................................... 7

Multivariate analysis ................................................................................................................................. 10

Limitations ................................................................................................................................................. 11

3. Descriptive statistics ....................................................................................................... 13

Shock exposure ........................................................................................................................................ 13

Household resilience capacity ............................................................................................................... 16

Coping strategies...................................................................................................................................... 19

NGO and government support ............................................................................................................ 21

Well-being/development outcomes ..................................................................................................... 24

4. Results from multivariate equations combining data over four years ...................... 26

Coping strategies 2013-2016................................................................................................................. 27

Well-being outcomes 2013-2016 ......................................................................................................... 28

DFSAs and CSI .......................................................................................................................................... 32

5. Results from equations for 2013, 2014, 2015 and 2016 ............................................... 34

Price shocks ............................................................................................................................................... 34

NGO and government support ............................................................................................................ 35

Elasticities ................................................................................................................................................... 40

6. Summary ......................................................................................................................... 41

7. Recommendations .......................................................................................................... 42

Appendix A: Regression equations – four years combined .............................................. 44

Appendix B: Regression equations – year by year ............................................................. 47

Appendix C: The household resilience capacity index ...................................................... 55

Appendix D: Relationships between household resilience capacity elements and

well-being outcomes 2013, 2014, 2015, 2016 .............................................. 57

Appendix E: Comparing the effects of explanatory variables .......................................... 89


TABLE OF TABLES v

Table of Tables

Table 1: Sample sizes, non-DFSA vs DFSA, 2013-2016................................................................................... 7

Table 2: Livestock ownership and unexpected losses (% HH) .................................................................... 15

Table 3: Household exposure to shocks in 2013 and 2014 (%HH) ........................................................... 15

Table 4: Shock exposure 2016 ............................................................................................................................ 16

Table 5: Severity of shocks .................................................................................................................................. 16

Table 6: Household resilience capacity elements ........................................................................................... 17

Table 7: Household resilience capacity elements – factor loadings ........................................................... 18

Table 8: Coping strategies index (CSI) 2013-2016 ......................................................................................... 20

Table 9: Non-food coping strategies (% HH) .................................................................................................. 21

Table 10: NGO and government support ........................................................................................................ 23

Table 11: NGO and government support by DFSA ...................................................................................... 23

Table 12: Well-being outcomes .......................................................................................................................... 25

Table 13: Results from regression equation (Tobit) estimating CSI, 2013-2016 .................................... 44

Table 14: Results from regression equation (logit) estimating negative coping strategies 2014-2016

.................................................................................................................................................................. 45

Table 15: Results from regression equations estimating well-being outcomes over four years ......... 46

Table 16: Results from regression equation (Tobit) estimating CSI, 2013 ............................................... 47

Table 17: Results from regression equations estimating well-being outcomes, 2013 ............................ 48

Table 18: Results from regression equations estimating coping strategies, 2014 ................................... 49

Table 19: Results from regression equations estimating well-being outcomes, 2014 ............................ 50


Table 21: Results from equations estimating well-being outcomes, 2015 ................................................ 52


Table 23: Results of equations estimating well-being outcomes, 2016 ...................................................... 54

Table 24: Household resilience capacity elements ......................................................................................... 55

Table 25: Relationships between elements of household resilience capacity & adequate food

consumption 2013 ................................................................................................................................ 57

Table 26: Relationships between elements of household resilience capacity and HDDS 2013 ........... 59


TABLE OF TABLES vi

Table 27: Relationships between elements of household resilience capacity & per capita daily

expenditures 2013 ................................................................................................................................ 61

Table 28: Relationships between resilience capacity & moderate to severe hunger 2013 ................... 63

Table 29: Relationships between household resilience capacity elements and adequate food

consumption 2014 ................................................................................................................................ 65

Table 30: Relationships between household resilience capacity elements and HDDS 2014 ............... 67

Table 31: Relationships between household resilience capacity elements and per capita daily

expenditures 2014 ................................................................................................................................ 69

Table 32: Relationships between household resilience capacity elements and moderate to severe

hunger 2014 ........................................................................................................................................... 71


consumption 2015 ................................................................................................................................ 73



expenditures 2015 ................................................................................................................................ 77

Table 36: Relationships between househld resilience capacity elements and moderate to severe

hunger 2015 ........................................................................................................................................... 79


consumption 2016 ................................................................................................................................ 81



expenditures 2016 ................................................................................................................................ 85

Table 40: Relationships between household resilience capacity elements and moderate to severe

hunger 2016 ........................................................................................................................................... 87


TABLE OF FIGURES vii

Table of Figures

Figure 1: Relationship of TANGO/USAID and ZimVAC variables to absorptive, adaptive and

transformative capacities ...................................................................................................................... 9

Figure 2: Monthly rainfall (mm), June 2012-May 2016 ................................................................................... 14

Figure 3: Results from regression equation (Tobit) estimating CSI, 2013-2016 ..................................... 27

Figure 4: Results from regression equation (logit) estimating negative coping strategies,

2014-2016 .............................................................................................................................................. 28

Figure 5: Results from regression equations estimating adequate food consumption, 2013-2016 ..... 29

Figure 6: Results from regression equation (OLS) estimating HDDS, 2013-2016 .................................. 30

Figure 7: Results from regression equation (GLM) estimating per capita daily expenditures

(USD 2016), 2013-2016 ...................................................................................................................... 31

Figure 8: Results from a regression equation (logit) estimating moderate to severe hunger,

2013-2016 .............................................................................................................................................. 32

Figure 9: Results from equation estimating CSI (Tobit), by DFSA and non-DFSA wards ..................... 33

Figure 10: Results of equation estimating per capita daily expenditures (USD2016) – shocks and

household resilience capacity .......................................................................................................... 35

Figure 11: Results from equations measuring well-being outcomes, 2015 ............................................... 37

Figure 12: Results from equations estimating well-being outcomes, 2016 ............................................... 39

Figure 13: Comparing the effects of resilience, NGO/govt. support, and shocks on CSI, 2013 ......... 90

Figure 14: Comparing the effects of resilience, NGO/govt. support, coping strategies and

shocks on outcomes, 2013 ............................................................................................................... 91

Figure 15: Comparing the effects of resilience, NGO/govt. support, and shocks on coping

strategies 2014 .................................................................................................................................... 93




strategies 2015 .................................................................................................................................... 97




strategies, 2016 ................................................................................................................................... 99


shocks on outcomes, 2016 ............................................................................................................. 100


ACRONYMS viii

Acronyms

AFDM African Flood and Drought Monitor

CBO Community-based organization

CFA Cash for assets

CI Confidence interval

CSI (Food) Coping strategies index

DFSA Development Food Security Activity

ENSURE Enhancing Nutrition, Stepping up Resilience and Enterprise

FANTA Food and Nutrition Technical Assistance

FCS Food consumption score

FEWS NET Famine Early Warning Systems Network

FFA Food for assets

FFP Food for Peace

FNC Food and Nutrition Council

FSN Formal safety net

GLM Generalized linear model

GMB Grain Marketing Board

HDDS Household dietary diversity score

HH Household

HHS Household hunger scale

IGA Income-generating activity

IR Intermediate result

ISAL Internal savings and lending

KMO Kaiser–Meyer–Olkin (statistical test)

MFI Microfinance Institution

NGO Non-governmental organization

PCA Principal component analysis

SACCO Savings and Credit Cooperative Organization

SAFIRE Southern Alliance for Indigenous Resources

SO Strategic objective

SNV Stichting Nederlandse Vrijwilliger Netherlands Development Organization

TLU Tropical livestock units

USAID United States Agency for International Development

USD United States dollars

VS&L Village savings and lending

WASH Water, Sanitation, and Hygiene

WFP World Food Programme

ZimVAC Zimbabwe Vulnerability Assessment Committee


EXECUTIVE SUMMARY ix

Executive Summary

This study adapted a USAID/TANGO resilience analysis framework to use with secondary datasets.

The goals were to describe the relationships between resilience capacity and well-being outcomes

in the face of a drought (adequate food consumption, household dietary diversity score, per capita

daily expenditures, and moderate to severe hunger), to empirically test whether resilience capacity

mitigates the effects of shocks on well-being outcomes, and to better understand the relationships

between programming, resilience capacity, and well-being outcomes.

The study covers four provinces in Zimbabwe – Manicaland, Matabeleland North, Matabeleland

South, and Masvingo1 – from 2013 through 2016. The provinces were chosen because they are sites

for USAID/Zimbabwe Development Food Security Activities (DFSAs). The study uses several

secondary data sources: ZimVAC household surveys from 2013, 2014, 2015, and 2016, ZimVAC

community surveys from 2014 and 2015, precipitation estimates from the Africa Flood and Drought

Monitor (AFDM), and World Food Programme (WFP) price data of key commodities. ZimVAC

household and community survey data are coded to identify wards with DFSAs and non-DFSA

wards. The initial study design was to compare households in DFSA wards to households in wards

without a DFSA. However, household survey data show that households in both DFSA and non-

DFSA wards are receiving similar programming (water and sanitation, food and cash support,

agriculture and veterinary services, and credit programs). The analysis shifted to examine the

relationship between types of programming and well-being outcomes. The key feature of the

USAID/TANGO methods was the computation of a household resilience capacity index to use in

multivariate regression equations. Equations tested whether increased household resilience capacity

is associated with better well-being outcomes, and whether household resilience capacity mitigates

the effect of shocks on well-being outcomes.

El Niño induced droughts in 2015 and 2016, and caused two successive crop failures. By 2016, all

households were under extreme and increasing stress. Living conditions were worsened by a drop

in the value of remittances in 2016 due to devaluation of the South African rand, and by macro-

economic conditions in Zimbabwe. A cash shortage nationwide in 2016 meant that workers were

not being paid on time or at all, the Grain Management Board (GMB) was late making payments to

farmers, and formal financial institutions were unable to provide credit to rural farmers and

livestock owners. Overall, in terms of shock exposure, coping strategies, and well-being outcomes,

people were worse off in 2015 than they were in 2014, and these conditions and outcomes further

deteriorated in 2016.

The drought was already underway in 2015, as DFSA implementation was ongoing. This, in addition

to macro-economic issues, severely curtailed the ability of programming to build resilience capacity.

In turn, this limited households' ability to cope with the droughts. Following the failed harvest in

1 The 2015 dataset includes 23 households in Midlands province.


EXECUTIVE SUMMARY x

2015, DFSAs expanded to include supplementary feeding and scale-up cash for assets (CFA). These

were further expanded when the 2016 harvest also failed.

Findings from this study document difficulties building resilience capacity during a prolonged

drought and unstable macro-economic conditions. Data from ZimVAC and other sources

document worsening drought in 2015 and 2016 and increasing downstream shocks to households.

Household survey data also show deterioration of assets and social capital, and lower levels of well-

being. In general, households drew down cereal stores, livestock assets, and savings over the course

of the drought. Cereal stores decreased with the onset of the drought and continued to fall

throughout. Households were able to maintain some livestock and savings at or near pre-drought

levels through year one but not through the second year. In the second year of the drought, the

percentage of households owning livestock decreased and the percentage of households reporting

loss of all livestock doubled. The mean value of livestock assets (estimated in Tropical Livestock

Units), also decreased. Data also show that in 2015 few agricultural and livestock producers had

access to formal markets. Market access mirrors crop and livestock depletion. Use of formal

markets for agricultural products dropped by half from 2015 to 2016, coinciding with the large

share of households reporting crop failure. Use of livestock markets increased between 2015 and

2016, coinciding with drought-related diseases2 and destocking programs in 20163.

Even though shocks were worsening and assets were being depleted, food coping strategies and

some non-food coping strategies improved or did not continue to worsen. Important exceptions

are withdrawing children from school, which did not increase until the second year of the drought,

and selling the last breeding female livestock, which increased in both years. An increased

percentage of households that sold their last breeding female livestock in year two of the drought is

consistent with findings discussed in the previous paragraph.

Program documentation and household survey data show that program emphasis shifted from a

development focus to emergency relief. This may explain why coping strategy index (CSI) scores

improved and some negative coping strategies did not increase in both years.

The study included as well-being outcomes: adequate food security, the household dietary diversity

score (HDDS), per capita daily expenditures, moderate to severe hunger and recovery (2016 only).

All four outcomes deteriorated over the course of the drought. The percentage of households

reporting adequate food consumption fell in both years. The HDDS and the percentage of

households reporting moderate to severe hunger did not worsen until year two of the drought.

Households may have been able to maintain HDDS by substituting less nutritious foods or

consuming nutritious food less often. The sharp increase in household hunger in year two may be

due to similar reasons: food shortages did not occur until the second year. Per capita daily

2 https://www.pressreader.com/zimbabwe/sunday-news-zimbabwe/20160828/281938837341395

3 https://zimbabweland.wordpress.com/2016/02/22/the-el-nino-drought-hits-livestock-hard-in-zimbabwe/

https://www.pressreader.com/zimbabwe/sunday-news-zimbabwe/20160828/281938837341395

https://zimbabweland.wordpress.com/2016/02/22/the-el-nino-drought-hits-livestock-hard-in-zimbabwe/


EXECUTIVE SUMMARY xi

expenditures dropped in year one and stayed at lower levels than before the drought. As of 2016,

almost no one reported any recovery.

Multivariate analysis shows that household resilience capacity is associated with improvements in all

well-being outcomes. In some cases (with HDDS over all four years and per capita daily

expenditures in 2016) household resilience capacity has a larger effect on households as shock

exposure increases, helping to mitigate the effects of shocks. Analysis also shows that agricultural

and/or livestock support is associated with improvements in nearly all well-being outcomes. Formal

safety nets generally improve food-related outcomes, and credit increases per capita daily

expenditures. Results from analyses of data from 2014 show that low producer prices (measured by

goat prices) and high consumer prices (measured by maize or maize meal) have large effects on

household well-being, even outside of drought conditions.


INTRODUCTION 1

1. Introduction

The objective of this research is to utilize secondary data from a variety of sources within a

USAID/TANGO resilience analytical framework to better understand how resilience capacity can

buffer the negative effects of shocks on well-being in Zimbabwe. In particular, the research

examines factors that can provide information about resilience programming in Zimbabwe. The

study includes wards in four provinces – Manicaland, Matabeleland North, Matabeleland South, and

Masvingo4 – and covers four years from 2013 through 2016. Surveys were conducted in May of

each year. Development Food Security Activities (DFSAs) were funded in 2013 and implementation

started in late 2014. Data from 2013 and 2014 provide a baseline for analysis, describing conditions

prior to DFSA implementation and prior to two years of drought. Data from 2015 and 2016

describe household resilience capacity and well-being in the face of drought and with DFSAs in

place. In addition, data cover wards in both DFSA and non-DFSA areas, allowing comparison.

Across the study area, two years of El Niño-induced droughts affected everyone. In 2015 and 2016,

all households were under extreme and increasing stress. Overall, in terms of shock exposure,

coping strategies, and well-being outcomes, people were worse off in 2015 than they were in 2014,

and these outcomes continued to decline in 2016. The drought was already underway in 2015 when

Development Food Security Activities began implementation. This severely curtailed programming

effectiveness in building resilience capacity. In turn, this limited households' ability to cope with the

droughts. In addition, following the failed harvest in 2015, Development Food Security Activities

shifted emphasis to expand supplementary feeding and scale-up cash for assets (CFA) activities.

These were further expanded when the 2016 harvest also failed.

This report examines changes in relationships over time between shocks and well-being outcomes,

as well as the effects of household resilience capacity, humanitarian, and development programming

(in both non-DFSA and DFSA wards) on household well-being outcomes.

The research questions are:

Are resilience capacities associated with improvements in coping strategies and well-being

outcomes?

Do resilience capacities help buffer the negative effects of shocks on well-being outcomes?

Is programming associated with increased resilience capacity and improvements in coping

strategies and well-being outcomes?

Of the elements directly related to programming, which are the strongest predictors of

improved well-being outcomes?

4 The 2015 dataset includes 23 households in Midlands province.


INTRODUCTION 2

In this study, well-being outcomes are measured by the following indicators: adequate food

consumption, household dietary diversity score (HDDS), per capita expenditures, and moderate to

severe household hunger. Coping strategies are measured using the food coping strategies index

(CSI) and the use of negative coping strategies. Shocks are measured by exposure to drought (using

satellite data obtained from ADFM), producer and consumer prices, and self-reported shock

exposure. Resilience capacities are measured as a combination of livestock assets, cereal stores,

education of household members, social capital, livelihood diversification, savings, market

participation, and exposure to information. Access to non-governmental organization (NGO)

and/or government programming is measured by whether a household received agricultural or

livestock assistance, improved water and sanitation, formal safety nets (FSN), and loans from other

than family and friends.

Description of USAID/DFSA programming

Enhancing Nutrition, Stepping Up Resilience and Enterprise (ENSURE) is a USAID Food for Peace

Title II DFSA. The activity started in June 2013 and will end in June 2018. ENSURE is implemented

by World Vision (consortium lead), CARE, Stichting Nederlandse Vrijwilliger

Netherlands Development Organization (SNV), and Southern Alliance for Indigenous Resources

(SAFIRE). It is implemented in Manicaland and Masvingo provinces. ENSURE targets vulnerable, food

insecure communities and works in the areas of nutrition and health, agriculture-focused income

generation, and household and community resilience. The goal is to improve the food security of

targeted communities and households in Manicaland and Masvingo provinces by 2018.

The strategic objectives (SOs) and intermediate results (IRs) of ENSURE are as follows:

SO1: Nutrition among women of reproductive age and children under 5 improved

IR1.1: Consumption of nutritious food Improved

IR1.2: Prevalence of diarrhea in children under 5 reduced

SO2: Household income increased

IR2.1: Agricultural productivity and production increased

IR2.2: Increased net revenue from targeted value chains

SO3: Resilience to food insecurity of communities improved

IR3.1: Community disaster preparedness and management capacities improve

IR3.2: Access to and management of disaster risk and mitigation assets improved

The focus of the development activity is multi-sectoral, achieving change via empowerment and

training activities, and service provision. As described in activity documents,5 the key vehicles for

5 USAID 2016, 2015. Annual Results Reports for World Vision Zimbabwe ENSURE DFSA, award AID-FFP-A-13-00003; FY

2016 and FY 2015.


INTRODUCTION 3

driving behavior change among program participants are via four cohesive groups of praxis: care

groups (nutrition), production and marketing groups (agricultural income generation), village savings

and lending (VS&L) groups (income generation), and disaster management committees (resilience),

all of which are supported by a strong gender equity training and empowerment component. In the

area of resilience, a robust food for assets (FFA) intervention enables ENSURE communities to

engage in infrastructure development that helps them to address vulnerabilities and risks –

especially related to drought – that are major underlying causes of food insecurity.

Major activity features include:

Providing supplementary and protective rations for pregnant and lactating mothers and

children 6-23 months to address critical nutrition needs related to the first 1,000 days of

life.

Working via VS&L groups to provide household-level financing for agricultural input

purchases, infrastructure maintenance, latrine construction, and small income-generating

activities (IGAs).

Addressing drought conditions by building climate change awareness, developing irrigation

infrastructure, and promoting climate-smart agriculture.

Lean season assistance activity serving close to 300,000 food insecure people (end of fiscal

year 2016)

Addressing gender issues via training and dialogues with women and men.

The Amalima program6, implemented by Cultivating New Frontiers in Agriculture (CNFA) was

funded in 2013 through 2018. The program’s name, Amalima, is the word for 'social contract', the

Ndebele custom by which families come together to help each other. Amalima is a USAID Food for

Peace-funded development activity operating in Matabeleland North and South. The activity

builds on existing community programs to strengthen food security and improve resilience. Amalima

provides supplementary food to pregnant and lactating women and children under the age of two,

and training on child care, hygiene and feeding practices7. Amalima also provides vouchers to

purchase productive assets such as goats and inputs, and utilizes matching grants to help producer

groups scale up production, as well as providing training in agricultural and livestock practices8.

6 USAID. 2016, 2015. Annual Results Reports for CNFA Zimbabwe, Amalima project. Award number: CNFA FFP-A-13-00004, FY

2016, 2015. 7 USAID. 2014. USAID Food for Peace Program, Amalima, supports rural households. https://www.usaid.gov/zimbabwe/press-

releases/usaid-food-peace-program-amalima-supports-rural-households 8 USAID. 2016, 2015. Annual Results Reports for CNFA Zimbabwe, Amalima project. Ibid.


INTRODUCTION 4

The strategic objectives of Amalima are:

SO 1: Household access to and availability of food improved

IR 1.1: Agricultural production and productivity Improved

IR 1.2 Agricultural marketing improved

IR 1.3 Post harvest losses reduced

SO 2: Community Resilience to Shocks Improved

IR 2.1 Agricultural basic infrastructure and other production assets developed/rehabilitated

IR 2.2 Community social capital leveraged

IR 2.3: Community-managed disaster risk reduction systems strengthened

SO 3: Nutrition and health among pregnant and lactating women; and boys and girls under 2

improved

IR 3.1 Consumption of diverse and sufficient foods for pregnant and lactating women; and

boys and girls under 2 improved

IR 3.2 Health and hygiene and caring practices of pregnant and lactating women, caregivers

and boys and girls under 2 improved

IR 3.3 Accessibility to and effectiveness of community health and hygiene services improved

Droughts and, to a lesser extent, macro-economic financial conditions impacted DFSA

programming in 2015 and 2016. As the drought progressed into 2016,9 a second year of crops

failed, livestock deaths increased, and widespread livestock disease (hoof and mouth, tick borne

diseases, Anthrax, and lumpy skin) were reported in several districts. Cattle were particularly hard

hit. DFSA emphasis shifted by suspending or curtailing livestock and agricultural support programs

and expanding cash for assets (CFA), food for assets (FFA), supplemental feeding, rations, and

voucher programs.

Among Zimbabwe's macro-economic issues are a cash shortage, induced by government spending

and restrictions on foreign investment.10 The shortage was exacerbated by a subsequent run on

banks and is expected to continue.11 Lack of liquidity means that workers and agricultural and

livestock producers were not getting paid. Commercial financial institutions stopped providing

credit to rural farmers. Internationally, the value of remittances from South Africa decreased as the

rand continued to fall in value against the US dollar. These added to difficulties for both households

and DFSA implementing agencies.

Data used in this analysis cover 2013-2016 and include household, ward, and district level

information from a variety of secondary sources (specific sources are listed in Section 3 of this

9 USAID. 2016. Annual Results Report: ENSURE. Ibid, USAID. 2016. Annual Results Report: Amalina. Ibid 10 https://www.thestandard.co.zw/2017/07/16/imf-raises-red-flag-cash-crisis/ 11 https://www.thestandard.co.zw/2017/07/16/imf-raises-red-flag-cash-crisis/, Ibid.

https://www.thestandard.co.zw/2017/07/16/imf-raises-red-flag-cash-crisis/

https://www.thestandard.co.zw/2017/07/16/imf-raises-red-flag-cash-crisis/


INTRODUCTION 5

report) for areas with DFSA programming and without DFSA programming. The 2013 and 2014

data provide baseline levels of households coping strategies and well-being outcomes prior to and in

early stages of DFSA programming. Data from 2015 and 2016 cover a period of extreme drought

and are used to analyze DFSA programming in the face of shocks.

The remainder of the report is organized as follows: Section 2 describes the data sources and

methodology, and discusses limitations to the study. Section 3 presents descriptive statistics;

covering shock exposure, elements of household resilience capacity and household resilience

capacity scores, the coping strategies index (CSI) and negative coping strategies, well-being

outcomes (adequate food consumption, HDDS, per capita daily expenditures, moderate to severe

hunger), and recovery (self-reported recovery was collected in 2016 only), as well as NGO and

government assistance. Section 4 reports the results of multivariate analysis using combined data

from 2013-2016 to show predicted values of coping strategies and well-being outcomes

corresponding to levels of household resilience capacity and shock exposure and changes over four

years in the coping strategies index (CSI), comparing households in DFSA vs non-DFSA wards.

Section 5 presents results from multivariate analysis of data year by year to show predicted values

of well-being outcomes at different levels of household resilience capacity, and changes in well-being

outcomes corresponding to types of NGO and government assistance, and the relationship

between prices and well-being outcomes. Section 6 is a summary of findings and recommendations.


METHODOLOGY 6

2. Methodology

This study applied a modified USAID/TANGO resilience analysis method to ZimVAC survey data.

The resilience analysis methods were originally developed to utilize survey data collected specifically

for resilience analysis,12 but have been modified over time to use data collected for other

purposes.13 For the Zimbabwe dataset used here, the resilience analysis methods have been

tailored, due to some differences between the ZimVAC dataset and datasets designed specifically to

measure resilience that would otherwise pose limitations for analysis.

Data sources

Data for this study come from several secondary sources: ZimVAC household surveys, the African

Flood and Drought Monitor (AFDM) (precipitation data),14 ZimVAC community surveys, and

World Food Programme (WFP) consumer price data.15

ZimVAC household and community survey data were provided by USAID/Zimbabwe and are

subsets of national datasets. Each of the four years is an independent sample. Datasets include

information from households in wards with and without DFSAs. Surveys took place in mid-May of

each year, during harvest season. Sample sizes are shown in Table 1. Detailed information about

survey methodology and results is reported in annual rural livelihoods assessment reports.16

12 Smith, L., T. Frankenberger, B. Langworthy, S. Martin, T. Spangler, S. Nelson, and J. Downen. 2015. Ethiopia Pastoralist Areas

Resilience Improvement and Market expansion (PRIME) Project impact evaluation baseline survey report. Report for USAID Feed the

Future FEEDBACK project. January.

Feed the Future FEEDBACK. 2015. Feed the Future Northern Kenya Resilience and Economic Growth in Arid Lands Impact Evaluation

Midline Report. Rockville, MD: Westat. December.

Frankenberger, T and L. Smith. 2015. Ethiopia Pastoralist Areas Resilience Improvement and Market Expansion (PRIME) Project Impact

Evaluation: Report of the Interim Monitoring Survey 2014-2015. Report for USAID Feed the Future FEEDBACK project. January.

September.

Langworthy, M., M. Vallet, S. Martin, T. Bower and T. Aziz. 2016. Baseline Study of the Enhancing Resilience and Economic Growth in

Somalia Program. Submitted by TANGO International to Save the Children Federation, December.

TANGO International. 2016. Building Resilience and Adaptation to Climates Extremes and Disasters (BRACED) Monitoring and

Evaluation. Report prepared for DFID.

TANGO International, 2016, Zimbabwe Resilience Research Initiative (ZRRI) Final report. October 31.

TANGO International, 2017, Nepal Resilience Research Report. Final report. May 4. 13 Smith, L. C. and T. R. Frankenberger. 2016. Does resilience capacity reduce the negative impact of shocks on household food

security? Evidence from the 2014 floods in Northern Bangladesh. Working paper.

TANGO International. 2016. Malawi IMS3 Resilience Analysis. Report prepared for USAID. October.

14 African Flood and Drought Monitor (AFDM). 2017. Accessed at:

http://stream.princeton.edu:9090/dods/AFRICAN_WATER_CYCLE_MONITOR/3B42RT_BC/MONTHLY.ascii? 15 WFP consumer price data accessed at: http://dataviz.vam.wfp.org/economic_explorer/prices 16 ZimVAC. 2013. Rural livelihoods assessment.

http://www.fnc.org.zw/downloads/zimvac%20reports/zimvac%202013/2013%20Rural%20Livelihoods%20Assessment%20Report.

pdf

ZimVAC 2014 Rural livelihoods assessment.

http://www.fnc.org.zw/downloads/zimvac%20reports/zimvac%202014/ZimVAC%202014%20FINAL_web.pdf

http://dataviz.vam.wfp.org/economic_explorer/prices

http://www.fnc.org.zw/downloads/zimvac%20reports/zimvac%202013/2013%20Rural%20Livelihoods%20Assessment%20Report.pdf

http://www.fnc.org.zw/downloads/zimvac%20reports/zimvac%202013/2013%20Rural%20Livelihoods%20Assessment%20Report.pdf

http://www.fnc.org.zw/downloads/zimvac%20reports/zimvac%202014/ZimVAC%202014%20FINAL_web.pdf


METHODOLOGY 7

Table 1: Sample sizes, non-DFSA vs DFSA, 2013-2016

Year Non-DFSA DFSA Total

2013 768 1,033 1,801

2014 779 1,020 1,799

2015 701 1,122 1,823

2016 1,032 1,332 2,364

Sources: ZimVAC (2013, 2014, 2015 2016) Household survey datasets

The African Flood and Drought Monitor (AFDM) is a real-time drought monitoring and seasonal

forecast system for sub-Saharan Africa developed through a collaboration of the United Nations

Educational, Scientific and Cultural Organization (UNESCO) and the International Hydrological

Programme. AFDM provided monthly estimates of precipitation (rainfall) based on satellite data.

These data are not the same as rainfall data collected using rainfall gauges at monitoring stations on

the ground. However, the data cover the study area in detail and are available for all four years.

Price data come from WFP and ZimVAC community surveys. ZimVAC community price data were

used in estimation equations for 2014 and 2015. WFP data were used in estimation equations for

2013.

Factor analysis – household resilience capacity index

USAID/TANGO resilience analysis methods typically use exploratory factor analysis to combine

data from community and household surveys to create three indexes measuring resilience

capacities: absorptive, adaptive, and transformative. Exploratory factor analysis is a multivariate

statistical method that uses the relationship among observed variables to identify one or more

underlying factors,17 See appendix 3 for a detailed description of the USAID/TANGO methods to

compute resilience capacity elements and index.

ZimVAC surveys did not include all the variables typically needed for computing the resilience

capacities, therefore some of the components for each capacity were adjusted to accommodate the

ZimVAC data. For example, the social capital index typically is computed based on responses to

questions about whether a household could receive (or give) food, cash, crops or WASH in the

event of a shock. Social capital in ZimVAC is based on whether a household actually received food,

cash, crops or WASH. In addition, the ZimVAC survey data lack the detail needed to compute

bonding and bridging social capital separately. Separate measures of bonding and bridging social

capital are key to differentiating between absorptive and adaptive capacities. In addition, community


http://www.fnc.org.zw/downloads/zimvac%20reports/zimvac%202015/2015%20ZimVAC%20Report%20_.pdf


http://www.fnc.org.zw/downloads/Bulletins/2016%20Bulletins/ZimVAC%202016%20Rural%20Livelihoods%20Assessment.pdf 17 Kim, J. & C. W. Mueller. 1978. Factor Analysis. Sage publications


METHODOLOGY 8

data are not available for all four years, and in those years for which they are available, the data lack

measures of most of the variables needed to compute transformative capacity. Therefore, instead of

three indexes, this study uses a single household resilience capacity index for each year (See

appendix 3 for computational details). Figure 1 shows how the standard (“USAID/TANGO”)

resilience variables and the variables available in the ZimVAC data correspond and feed into the

computation of the three resilience capacity indexes. The three indexes are: absorptive, adaptive

and transformation capacity indexes. They are then combined into an overall resilience capacity

index. This report uses the terms “household resilience capacity” to refer to the single index

computed from the ZimVAC data. Household resilience capacity is a streamlined combination of

absorptive and adaptive capacities from the USAID/TANGO methods. It contains only household

level information.


METHODOLOGY 9

Figure 1:

Relationship of TANGO/USAID and ZimVAC variables to absorptive, adaptive and transformative capacities

Absorptive TransformativeAdaptive

Savings BridgingBonding AssetsHuman

capital

Info

exposure

Livelihood

riskLinking

Social

capital*Education*

TANGO/

USAID

ZimVAC SavingsCereals*

LivestockLivelihood

risk

Household

Community***

ISN

ISN**

Disaster

prep

* Variables used to compute measures are different than in other TANGO/USAID studies

** Household data were used to compute these.

*** Community data are available for 2014 and 2015.

Natural

resourcesFSN

Market

access

FSN**

Public

services

ZimVAC

TANGO

/USAID

Market

accessRoadsIrrigation


METHODOLOGY 10

Multivariate analysis

The analysis uses multivariate regression analysis to estimate household use of coping strategies and

household well-being outcomes. The key feature of the analyses is an interaction term to test

whether household resilience capacity mitigates the effects of shocks on coping strategies and well-

being outcomes. The interaction term is equal to the household resilience capacity index score

multiplied by the shock measure. Multivariate equations are defined as follows:

Coping strategies:

Coping strategies index (CSI)= f(HH resilience capacity * shock exposure, programming

variables, HH characteristics and geographic controls). A censored regression equation

(Tobit) estimates CSI.

Negative coping strategies= f(HH resilience capacity * shock exposure, programming

variables, HH characteristics and geographic controls). The dependent variable is coded 0

if equal to 'no' and 1 if equal to 'yes'. A logit equation estimates the probability that a

household engages in negative coping strategies.

Well-being outcomes:

Adequate food consumption= f(HH resilience capacity * shock exposure, coping

strategies, programming variables, HH characteristics and geographic controls). The

dependent variable is coded 0 if equal to 'no' and 1 if equal to 'yes'. A logit equation

estimates the probability of adequate food consumption.

HDDS= f(HH resilience capacity * shock exposure, coping strategies, programming

variables, HH characteristics and geographic controls). An Ordinary Least Squares (OLS)

regression estimates HDDS.

Per capita daily expenditures (USD 2016)= f(HH resilience capacity * shock exposure,

coping strategies, programming variables, HH characteristics and geographic controls). A

Generalized Linear Model (GLM) is used to estimate per capita daily expenditures.

Moderate to severe hungers= f(HH resilience capacity * shock exposure, coping

strategies, programming variables, HH characteristics and geographic controls). The

dependent variable is coded 0 if equal to 'no' and 1 if equal to 'yes'. A logit equation

estimates the probability of moderate to severe hunger.

A similar USAID/TANGO study18 allowed estimation of a simultaneous regression equation to

examine changes in well-being outcomes associated with programming designed to strengthen

18 TANGO International. 2017. Nepal Resilience Research Report.


METHODOLOGY 11

absorptive, adaptive and transformative capacities. The hypothesis being tested was that program

activities improve these capacities, which in turn buffer the effects of shocks on well-being

outcomes. In that study, programming variables were statistically significant in multivariate equations

estimating absorptive, adaptive and transformative capacities but not in equations estimating well-

being outcomes, making estimation using a simultaneous equation mathematically possible and

analytically appropriate.19 This study tested the relationships between programming variables,

household resilience capacity, and well-being outcomes to see if the same model was appropriate.

Programming variables were statistically significant in equations estimating resilience capacity and in

equations estimating well-being outcomes so it is not possible to estimate a simultaneous equation.

Instead, programming variables are included in equations estimating coping strategies and well-being

outcomes directly.

Limitations

Household surveys did not collect the same information in all four years. Consequently, household

resilience capacity indexes use different variables for each year. In addition, the difference in

variables across years means that the factor loadings assigned to variables are not comparable

across years.

Except for 2016,20 household surveys do not include detailed information about exposure to

specific shocks. Shock exposure information is essential for resilience analysis. For 2013-2015 this

study uses information from AFDM (127-159 reporting sites, roughly corresponding to wards) and

from WFP (8 markets) and ZimVAC community surveys (2014 and 2015) to measure household

exposure to price-related shocks. Because these data are reported at a higher level of aggregation

than household data, there is less variation, making estimates less precise.

The 2016 household survey shock module has a limited list of shocks and does not include livestock

disease, crop disease, wild animals destroying livestock and crops, theft, fire, cash shortages or

conflict (shocks that were noted in the community survey and/or found to be important in other

studies).

Community data for 2014 and 2015 measure only a few of the elements of transformative capacity

(markets and roads) as it is computed by TANGO/USAID. Lack of community data for 2013 and

2016 mean that well-being estimates do not fully take into account community-level factors. This is

an important caveat for interpreting results of the current analysis, given that other studies have

shown the importance of transformative capacity and its elements such as infrastructure, access to

19 Wooldridge, Jeffrey M. 2006. Introductory Econometrics: A Modern Approach (Third edition.). Mason, OH: Thomson/South-

Western 20 The 2013 and 2014 surveys ask about a main shock.


METHODOLOGY 12

services, governance, natural resource management, conflict mitigation, and disaster planning for

household well-being.

Community level data on goat prices (as a proxy for producer prices) and maize or maize meal

prices (as a proxy for consumer prices) provided an objective measure of price shocks. Price data

were incorporated into estimation equations for 2014 and 2015.

ZimVAC household surveys do not collect sufficiently detailed data to compute several resilience

capacity elements according to USAID/TANGO methods. Livelihood diversification is one example.

In other similar studies, information to measure livelihoods comes from either the household roster

(questions about paid and unpaid work) or from a module asking specifically about livelihoods

activities. Both questions cover the past 12 months. In this study, information about livelihoods

came from an income module asking about income sources (cash or in-kind) over the past 30 days.

Having a 30-day recall means that households generally report fewer livelihoods than over 12

months, giving a less complete picture of how households diversify livelihoods to manage risk. As

mentioned earlier, surveys used in this analysis do not collect information about social capital in a

way that allows for computing bridging and bonding social capital that is consistent with

USAID/TANGO methods. Nor do the surveys contain the information needed to compute linking

social capital. Household survey questions about NGO and government programming are fairly

general and not consistent over survey years, making it difficult to estimate the relationship

between programing, household resilience capacity, and well-being outcomes.

Finally, because data are a subsample from a larger dataset, household sampling weights are

unknown. All statistics reported in this analysis were computed using unweighted data, which limit

the extent to which findings from sampled households can be generalized to the larger population.

Statistics computed using unweighted data have smaller standard errors than those computed using

weighted data, increasing the likelihood of ‘significant’ findings that may not actually be ‘true’.


DESCRIPTIVE STATISTICS 13

3. Descriptive statistics

This section provides descriptive statistics covering exposure to various shocks, the household

resilience capacity index and its elements, NGO/government support, coping strategies, and well-

being outcomes across years. Tables provide means or percentages for 2013-2016 and results of

pairwise tests comparing values between years. Meaningful differences with significance levels of

0.10 or better are presented in the tables in order to show findings that may be interesting but do

not meet the 0.05 standard for statistical significance.

Shock exposure

In Zimbabwe, as elsewhere, droughts typically trigger a series of downstream shocks. Lack of water

and pasture causes livestock to become emaciated and diseased, and some die; animals also become

more vulnerable to theft and predation. Herders (often children) travel further distances in search

of water and pasture, and cannot attend school because they are tending livestock. Livestock prices

fall because markets are over-supplied with sick and emaciated animals. Drought also causes crop

failure, and food prices to rise because of shortages. Farmers don’t have money for inputs.

Household members, especially those who work in agriculture and livestock, lose their jobs and

cannot afford to buy food. These events are documented using data from ZimVAC household

surveys and other secondary sources. Data from multiple sources: precipitation (AFDM 2017),

livestock and crop loss (ZimVAC household surveys 2013-2016), and price shocks (ZimVAC

community surveys 2014, 2015; WFP 2013) show high levels of exposure to drought and

downstream shocks in 2015 and increased exposure in 2016.

Precipitation estimates (June 2012 through May 2016) from AFDM are presented in Figure 2. The

figure shows monthly rainfall and 30-year mean monthly rainfall (103 mm) for the rainy season

(October-January21). The figure shows drought conditions followed by flooding in 2013, above

normal rainfall in 2014, then two years of drought in 2015 and 2016. Rainfall patterns shown in the

figure are noted in other sources. Examples are: early season drought in 2013,22,23 more evenly

distributed rainfall across the rainy season in 201424, and high rainfall (flooding) in December 2014.25

AFDM rainfall estimates provide data to compute mean monthly rainfall during the six months prior

to the survey (November-May), which is one of the shock exposure variables used in multivariate

regression equations.

21 ZimVAC. 2016. ZimVAC lean season monitoring report.

http://www.fnc.org.zw/downloads/Bulletins/2016%20Bulletins/ZimVAC%20Lean%20Season%20Monitoring%20Assessment.pdf 22 http://www.fao.org/emergencies/fao-in-action/projects/detail/en/c/240213/ 23 http://www.aljazeera.com/indepth/features/2013/04/2013416132856364607.html 24 ZimVAC. 2014. Reported that all provinces received normal to above normal rainfall. 25 International Federation of Red Cross and Red Crescent Societies (IFRCRC) 2015. Emergency Plan of Action (EPoA) Zimbabwe:

Floods.



Figure 2: Monthly rainfall (mm), June 2012-May 2016

Source: AFDM 2017

0

50

100

150

200

250

300

Ju

n-1

2

Se

p-1

2

De

c-1

2

Ma

r-1

3

Ju

n-1

3

Se

p-1

3

De

c-1

3

Ma

r-1

4

Ju

n-1

4

Se

p-1

4

De

c-1

4

Ma

r-1

5

Ju

n-1

5

Se

p-1

5

De

c-1

5

Ma

r-1

6

Ju

n-1

6

Ra

infa

ll (m

m)

Rainy season Nov-Jan

30 yr mean=130mm/mo

(Nov-Jan)

In ZimVAC surveys, households that owned livestock were asked about livestock deaths due to

drought/lack of water, disease, and predation. In survey years 2014, 2015, and 2016, households

were also asked about theft of livestock. Table 2 shows the share of households owning large

livestock (cattle, draught cattle, goats, and sheep) decreased as drought conditions worsened.

Livestock ownership was lower in 2016 than in any of the other years, dropping from between 67.2

to 69.6 percent in 2013-2015 to 64.6 percent in 2016. However, the proportion of households

reporting unexpected livestock losses over the four survey rounds does not differ significantly over

time. The last row of the table presents data on the proportion of households that lost remaining

livestock. This was computed as households that reported owning livestock in the year prior to the

survey but none in the survey year. The proportion of households reporting loss of remaining

livestock was lower in 2015 (2.2 percent) than previous years (5.4 percent in 2013 and 4.7 percent

in 2014). The proportion of households losing all their livestock increased again in 2016 (5.0

percent), providing additional evidence of the intensity of the drought.



Table 2: Livestock ownership and unexpected losses (% HH)

% HH n

2013 2014 2015 2016 2013 2014 2015 2016

Households owning

large livestock1

67.2 a 69.6 b 67.3 c 64.6 abc 1801 1799 1823 2364

Unexpected loss of

livestock2,3

38.7

43.7

41.7

42.0

1211 1253 1230 1527

Lost remaining

livestock4

5.4 a 4.7 b 2.2 abc 5.0 c 1215 1263 1198 1519

Subgroups with the same superscript are significantly different at the 0.10 level. Comparisons are across columns.

1. Cattle, draught cattle, goats, sheep 2. Includes households owning large livestock in survey year or the prior year.

3 Statistical tests do not include 2013 because information about livestock theft was not collected in 2013. 4 Includes households owning large livestock in the year prior to the survey.

Source: ZimVAC. 2013, 2014, 2015, 2016. Household surveys.

Findings: Households maintained livestock holdings through the first year of drought (2015); but in

the second year (2016), the percentage of households owning livestock decreased and the

percentage of households reporting loss of all livestock doubled.

In the 2013 and 2014 surveys (only), respondents were asked “Did your household experience a shock

that affected your household’s access to adequate cereals?”; and a subsequent question, “What was the

main shock?”. The results are reported in Table 3. Data show that the percentage of households

reporting exposure to shocks dropped between 2013 and 2014 (from 82.3 percent to 75.6

percent). In both years, of the households exposed to shocks, drought was reported as the main

shock.

Table 3: Household exposure to shocks in 2013 and 2014 (%HH)

% n

2013 2014 2013 2014

Exposed to any shock 82.3 a 75.6 a 1723 1657

Drought as main shock 73.8 a 64.4 a 1691 1556

Lack of inputs as main shock 13 13.4 1691 1556


Sources: ZimVAC. 2013, 2014. Household survey data.

The 2016 survey included a series of questions about exposure to 10 shocks, the impact of each

shock on food consumption and whether or not the household had recovered from the shock.26

Table 4 shows that crop- and livestock-related shocks are the most widely reported. More than

eight out of ten households experienced crop failure and/or cereal price changes.

26 The survey also asked about other impacts and which household members were the most affected.



Table 4: Shock exposure 2016

Shock exposure1 2016

% n

Crop failure 84.3 2332

Cereal price change 51.3 2308

Livestock deaths 25.1 2251

Livestock price change 17.5 2275

Health related2 10.1 2222

Loss of employment 4.0 2223

Death of main breadwinner 3.6 2219

1 Percentages sum to more than 100 because of multiple responses

2 Includes HIV/AIDS, diarrheal and malarial diseases

Source: ZimVAC. 2016. Household survey data.

The severity of shocks score is the mean value of a score ranging from 1 to 4, where higher scores

correspond to worse conditions in terms of household food consumption: 1 is an increase in food

consumption, 2 is no change, 3 is a moderate decline, and 4 is a severe decline. Table 5 shows that

for households exposed to shocks, nearly all shocks resulted in a moderate to severe decline in

food consumption.

Table 5: Severity of shocks

Shock severity1 2016

Mean score n

Cereal price change 3.5 1168

Livestock price change 3.3 381

Crop failure 3.6 1913

Livestock deaths 3.2 533

Death of main breadwinner 3.6 79

Health related2 3.1 215

1 Percentages sum to more than 100 because respondents of multiple responses.

2 Includes HIV/AIDS, diarrheal and malarial diseases

Source: ZimVAC. 2016. Household survey data.

Household resilience capacity

Table 6 shows changes in individual elements of household resilience capacity over the four years.

Detailed information about computing each element is provided in Appendix 3. In general,

households drew down assets and savings over the course of the drought. Data show that

households were able to maintain cereal stores through 2015, based on their estimated value in

2016 USD. They dropped, however, from an estimated high value of $57 in 2014 to $44 in 2016,

year two of the drought. Savings followed a similar pattern. Households maintained savings through

the first year of the drought (2014-2015), but they then dropped from $32.8 in 2015 to $21.2 in the



second year of the drought, a reduction of nearly one-third. Livestock holdings (Tropical livestock

units or TLU) increased between 2014 and 2015 (2.8 to 3.3 TLU) but then dropped in 2016 to 2.8

TLU.

Table 6: Household resilience capacity elements

2013 2014 2015 2016

Cereal stores (USD2016, mean) 52.8 a 57.0 b 52.2 c 44.3 abc

Livestock assets (TLU, mean) 2.5 ab 2.8 a 3.3 ab 2.8 b

ISN (0-5, mean) 0.02 ~ 0.02 0.02

Count of livelihoods (0-8, mean) 1.3 a 0.9 a 1.0 a 1.8 a

Education level head of household (1-8, mean) 2.3 a 2.4 b 2.3 c 2.5 abc

Count of adults in hh with more than primary

level education (mean) ~ ~ 1.0 a 1.1 a

Social capital (0-10, mean) 0.46 a ~ 0.39 a 0.41

Savings (USD2016, mean) 29.1 a 27.8 b 32.8 c 21.2 abc

Remittances (0-5, mean) 0.2 a ~ 0.2 b 0.3 ab

Sale of agricultural products to traders, GMG,

millers, markets, or contractors (%) ~ 2.8 a 3.7 b 1.9 ab

Sale of livestock products to traders, abattoirs,

contractors (%) ~ ~ 5.9 a 9.9 a

Information from government, NGOs, newspaper,

TV or Internet (0-8) (mean) ~ ~ 2.7 ~

N 1801 1791 1823 2364


~ Data were not collected.

Sources: ZimVAC. 2013, 2014, 2015, 2016. Household survey data.

Findings: Households drew down cereal stores, livestock assets, and savings over the course of the

drought. They were able to maintain some assets at or near pre-drought levels through year one of

the drought but not through two years of drought. Few agricultural and livestock producers had

access to formal markets. Use of formal markets for agricultural products dropped by one-half from

2015 to 2016. Lower cereal stores and use of markets in 2016 is supported by the large share of

households reporting crop failure (Table 4).

Elements are combined using exploratory factor analysis to compute the household resilience

capacity index (see section 2 for detail on methods). Table 7 shows the variables that make up the

household resilience capacity indexes (one for each year), whether or not they are in each dataset,

and factor loadings. Neither factor loadings nor household resilience capacity index scores are

comparable across years because the factors have different elements in each year. Factor loadings



are low27 but in line with similar studies.28 Eigenvalues, similarly are fairly low (close to one). An

eigenvalue of less than one means that the factor explains less of the variation among the variables

than each one separately. Kaiser–Meyer–Olkin (KMO) is a measure of sampling adequacy. KMO

takes values between 0 and 1, with small values meaning that overall the variables have too little in

common to warrant a factor analysis29. KMO scores for household resilience capacities are low, but

acceptable. Multivariate equations presented in Appendix 4 compare the relationship between

household resilience capacity and each element on coping strategies and outcomes.

Table 7: Household resilience capacity elements – factor loadings

2013 2014 2015 2016

Cereal stores (USD 2016) 0.48 0.37 0.49 0.54

Livestock (TLU)30 0.69 0.23 0.57 0.58

Education level head of household 0.31 0.38 c c

Savings 0.75 0.44 0.53 0.47

Count of adults in HH with more than primary level

education ~ ~ 0.45 0.42

ISN HH received food, cash, ag inputs, livestock inputs, or

WASH inputs from churches (0-5) § ~ § §

Social capital HH received food, cash, ag inputs, livestock

inputs, or WASH inputs from urban or rural relatives (0-10) § ~ § §

Remittances HH received food, cash, ag inputs, livestock

inputs, or WASH inputs as remittances (0-5) § ~ § §

Count of livelihoods (0-8) 0.20 0.31 0.33 0.43

Remittances as an income source (%) § 0.14 ‡ ‡

HH sold ag products to traders, GMB, millers, markets, or

contractors (%) ~ 0.29 0.39 0.37

HH sold livestock products to traders, CSC, markets, or

contractors (%) ~ ~ 0.42 0.39

Count of information types received from government,

NGOS, newspaper, TV or Internet (0-8)1 ~ ~ 0.53 ~

Eigenvalue 1.39 1.37 1.76 1.46

KMO2 0.52 0.60 0.62 0.58

Household resilience capacity index (0-100, mean) 7.3 16.8 7.1 7.5

n 1801 1791 1822 2364

§ Variable had a negative loading and was dropped from index.

~ Information was not included in ZimVAC survey.

‡ Information was included in survey but a different measure was used in index.

27 Kim, J. & C. W. Mueller. 1978. Ibid. 28 Factor loadings from other studies: loadings for household level variables used to compute in resilience capacity indexes

according to USAID/TANGO methods range from 0.03 to 0.49 (USAID Somalia, Niger, Burkina Faso). 29 Stata. https://www.stata.com/manuals13/mvfactorpostestimation.pdf. 30 Computed using methods described in Food and Agriculture Organization (FAO). 2011. Guidelines for the preparation of

livestock sector reviews.

https://www.stata.com/manuals13/mvfactorpostestimation.pdf



1 'Information types' is a count of whether the household received information about one of more of the following topics:

weather, rainfall, livestock, livestock prices, business, borrowing, food market prices, input markets, child feeding, or

health and that information came from: NGOs, CBOs or churches; government officials; newspaper; radio or TV; Internet

or SMS.

2 The following labels are given to values of KMO:

0.00 to 0.49 unacceptable

0.50 to 0.59 miserable

0.60 to 0.69 mediocre

0.70 to 0.79 middling

0.80 to 0.89 meritorious

0.90 to 1.00 marvelous

Stata. https://www.stata.com/manuals13/mvfactorpostestimation.pdf.

Coping strategies

Households engage in a number of different strategies to cope with shocks, and use increasingly

extreme or negative coping strategies over the course of a drought, usually starting by reducing

food consumption, then drawing down savings, selling household and productive assets, and selling

small livestock. When those assets are depleted, households sell large livestock, which are the most

valuable. Households without savings or assets cope by continuing to reduce food consumption,

begging, removing children from school, or sending children to work.

The Coping Strategy Index (CSI) is an index of food-related strategies computed on the basis of a

series of questions about how frequently31 respondents utilized each of the following 12 possible

strategies in the 30 days prior to the interview:

1. Skip entire days without eating

2. Limit/reduce portion size at mealtimes

3. Reduce number of meals eaten per day

4. Borrow food or rely on help from friends or relatives

5. Rely on less expensive or less preferred foods

6. Purchase/borrow food on credit

7. Gather/hunt unusual types or amounts of wild food

8. Harvest immature crops

9. Send household members to eat elsewhere

10. Send household members to beg

11. Reduce adult consumption so children can eat

12. Rely on casual labour for food

31 Response categories are: Never, Seldom (1-3 days per month), Sometimes (1-2 days per week), Often (3-6 days a week) or

Daily.

https://www.stata.com/manuals13/mvfactorpostestimation.pdf



The computation of the CSI follows methods developed by Maxwell, Caldwell, and Langworthy32

that involve weighting the frequency responses reported for each strategy to account for its

severity. Higher scores correspond to worse conditions, that is, use of more negative strategies to

deal with food shortages. Table 8 compares mean CSI scores from 2013-2016. The data show that

CSI dropped from 2013 to 2014 coinciding with a shift from low rainfall in 2013 to above normal

rainfall in 2014. Compared to 2014 (16.0), CSI scores were higher following the onset of the

drought in 2015 and 2016 (24.1 and 20.2, respectively). The drop between 2015 and 2016 may be

due to NGO and government programming.

Table 8: Coping strategies index (CSI) 2013-2016

2013 2014 2015 2016

CSI (mean) 32.7 a 16.0 a 24.1 a 20.2 a

n 1739 1799 1823 2363

Subgroups with the same superscript are significantly different at the 0.10 level. Comparisons are across columns

Source: ZimVAC. 2013, 2014, 2015, 2016. Household survey data.

Finding: CSI increased during the first year of drought but dropped during the second year. This

may be due to expanded cash, food, and voucher programs in 2016 compared to 2015.

Surveys in 2014, 2015, and 2016 include questions about non-food coping strategies over the 12

months prior to each survey. Table 9 shows household use of non-food coping strategies ranked by

2016 values. Overall, results suggest that households tended to use a similar mix of coping

strategies each year. Spending down savings, reducing non-food expenditures, and selling more

livestock than usual are the top three coping strategies in all years. Compared to 2014, households’

use of all non-food coping strategies, except for sale of household assets and withdrawing children

from school, increased in 2015 during the first year of the drought.33 The percentage of households

selling more livestock than usual and removing children from school continued to rise over the

course of the drought. In 2014, 3.4 percent of households reported selling more livestock than

usual, which rose to 8.3 percent in 2015 and to 10.6 percent in 2016. Households removing

children from school did not significantly increase until 2016, at which time it rose to 8.5 percent

(from an average of approximately 5 percent across 2014 and 2015).

32 Maxwell, Daniel, Richard Caldwell and Mark Langworthy. “Measuring food insecurity: Can an indicator based on localized

coping behaviors be used to compare across contexts?” Food Policy, Volume 33, Issue 6, December 2008.

33 Survey response codes for non-food coping strategies are ‘yes’ and ‘no’. For ‘no’ responses the respondent is also asked the

reason why. Possible responses are: 1) No, because it wasn't necessary; 2) No, because I already sold those assets or did this

activity within the last 12 months and I cannot continue to do it or; 3) No, I don’t have assets/savings/access. Households

were coded as engaging in a coping strategy if they replied ‘yes’ or if they replied ‘no, because they already did this activity or

depleted the source in past 12 months’.



Of the coping strategies, removing children from school, selling last female breeding livestock, and

sending household members to beg for food are considered to be negative coping strategies

because they tend to be methods of last resort and have both short- and long-term impacts on

well-being, especially for children. Table 9 also shows that the percentage of households who

engaged in at least one negative coping strategy was less than 10 percent in 2014. After the onset of

the drought in 2014, the percentage of households using at least one negative coping strategy

increased significantly, to 15.5 in 2015 and 16.9 in 2016.

Table 9: Non-food coping strategies (% HH)

2014 2015 2016

Spent savings 11.9 ab 21.0 a 20.9 b

Reduced non-food expenditures 7.0 ab 17.6 a 17.0 b

Sold more livestock than usual 3.7 a 8.3 a 10.6 a

Withdrew children from school 4.6 a 5.5 b 8.5 ab

Sold last breeding female livestock 3.6 a 9.5 a 7.7 a

Sold household assets 6.3 7.3 7.4

Engaged in begging 4.1 ab 5.3 a 5.2 b

Negative coping strategies1 9.2 ab 15.5 a 16.9 b

N 1758 1823 2364

1HH engaged in one or more of: withdrawing children from school, sending children to work, selling last remaining female

breeding livestock or begging.



Findings: The percentage of households using most non-food coping strategies increased during the

first year of the drought but did not increase again in year two. Exceptions are withdrawing children

from school, which did not increase until the second year of the drought, and selling last breeding

female livestock, which increased in both years. Livestock losses are consistent with data presented

in Table 2.

NGO and government support

This section describes support from NGOs and government. Household survey datasets provide

information to compute four measures of support: formal safety nets (FSN), improved water and/or

sanitation, loans, and agricultural and livestock support. Comparisons across years are provided in

Table 10. Additional comparisons (non-DFSA vs DFSA) are provided in Table 11. Information from

the household survey provides only a proxy for programming; the survey was not designed as a

program monitoring tool and therefore does not collect detailed information about individual

programs, participation levels, or specific sources of programming.



The FSN indicator represents the percentage of households reporting they received food, cash,

crop inputs, livestock, or WASH inputs from an NGO or the government during the 12 months

prior to a survey. Table 10 shows that the percentage of households who received formal

assistance from either a NGO or the government rose over the course of the drought, increasing

from 34.7 percent in 2015 to 51.9 percent in 2016. Surprisingly, the percentage in 2016 was lower

than in 2013, during a drought when other programming (prior to DFSA) was in place.

Loans include borrowing from one or more sources: traders, contractors, microfinance institutions

(MFIs), banks, savings and credit groups/ISALs/burial societies, cooperatives or SACCOs. The

percentage of households receiving loans decreased between 2014 and 2016, dropping from 8.8

percent in 2014 to 7.2 percent in 2016. This is coincident with cash shortages nationwide.

Improved water and improved sanitation are computed according to WHO/Unicef guidelines,34 which

consider households to have an improved water source if their water comes from piped water into

the dwelling or yard, public tap/standpipe, tube well/borehole, protected dug well, protected spring,

or rainwater. Improved sanitation facilities are: flush to piped sewer system, flush to septic tank,

flush/pour flush to pit, composting toilet, ventilated improved pit latrine, and a pit latrine with a slab.

Because shared and public facilities are often not as clean as private facilities, they are not

considered as improved (WHO and UNICEF 2006). The water and/or sanitation indicator is a

count (0-2) of whether a household has improved water, improved sanitation, or both. Table 10

shows an increase in improved water and/or sanitation, rising from 1.17 in 2014 to 1.24 in 2016.

Agricultural and livestock assistance measures whether a household received one or more of

agricultural training, cropping advice, or technical or veterinary support. Data are from 2015 and

2016 household surveys and include households reporting that they cultivated one or more crops

or own at least one animal. The data show a drop between 2015 and 2016 in the percentage of

households reporting that they received assistance (46.8 and 41.3 percent, respectively). This

coincides with the length of the drought and a second year of crop failure, as well as with the shift

in DFSA programming from development to emergency assistance.

34 WHO and Unicef. 2006. Core questions on drinking-water and sanitation for household surveys.

http://www.who.int/water_sanitation_health/monitoring/oms_brochure_core_questionsfinal24608.pdf



Table 10: NGO and government support

2013 2014 2015 2016

FSN (%) 53.9 a ~ 34.7 a 51.9 a

Loan (%) ~ 8.8 a 7.8 7.2 a

Water/sanitation (0-2, mean) 1.17 a 1.16 b ~ 1.24 ab

n 1801 1799 1826 2364

Livestock and/or crop assistance ~ ~ 49.0 a 42.6 a

n 1669 2264

~ Data were not collected.



Table 11 provides additional information about NGO and government support by comparing DFSA

with non-DFSA wards. Households in both DFSA and non-DFSA wards reported receiving

assistance from NGOs and/or government. Thus, these results suggest that non-DFSA wards are

not appropriate as a control group to measure the effects of DFSA programming. This finding

shapes the regression analysis described in Sections 4 and 0.

The data show that in 2013 and 2016, a larger proportion of households in DFSA wards received

formal assistance than did households in non-DFSA wards (60.1 versus 44.4 percent of households

in 2013 and 53.9 versus 49.2 percent in 2016). The percentage of households taking out loans was

similar in non-DFSA and DFSA wards for 2014 and 2015. However, in 2016, significantly more

households in non-DFSA than DFSA wards reported taking out a loan (9.4 compared to 5.6

percent, respectively). In both 2015 and 2016, more households in non-DFSA than DFSA wards

reported receiving agricultural and/or livestock support (52.0 and 47.2 percent in 2015, and 46.4

and 39.7 percent in 2016, respectively).

Findings: The type of NGO or government support received by households shifted over the course

of the drought: fewer households received agricultural and livestock assistance and more

households received FSN. Households in DFSA and non-DFSA wards reported receiving NGO

and/or government support, which means that non-DFSA wards do not constitute a 'control group'

for analyzing the effects of DFSA programming.

Table 11: NGO and government support by DFSA

2013 2014 2015 2016

non-

DFSA DFSA

non-

DFSA DFSA

non-

DFSA DFSA

non-

DFSA DFSA

FSN (%) 44.4 60.1 *** ~ ~ 33.0 35.6 49.2 53.9 ***

Loan ~ ~ 8.7 8.9 7.6 8.0 9.4 5.6 ***

Water/sanitation 1.13 1.19 1.0 1.3 *** ~ ~ 1.2 1.3 ***

n 768 1033 779 1020 701 1122 1032 1332



2013 2014 2015 2016

non-

DFSA DFSA

non-

DFSA DFSA

non-

DFSA DFSA

non-

DFSA DFSA

Livestock and/or

crop assistance ~ ~ ~ ~ 52.0 47.2 *** 46.4 39.7 ***

n 639 1027 991 1273 ~ Data were not collected.



Well-being/development outcomes

Table 12 presents the four well-being development outcomes (2013-2016) and recovery (2016

only). Well-being outcomes are: adequate food consumption, HDDS, per capita daily expenditures,

and moderate to severe hunger. Recovery includes households that experienced crop failure and/or

livestock death.

Adequate food consumption was derived from the food consumption score (FCS), which was

computed following methods developed by WFP.35 It is a weighted count of household

consumption of nine food groups over the seven days prior to a survey. Weights were developed

by WFP and reflect 'nutrient density' so that more nutritious foods, like meat and fish, have the

largest weight (4) and sugar and condiments have the smallest (0.5 and 0). The maximum possible

value is 140 (if a household consumed all food groups every day). The FCS is divided into three

categories: "Poor" corresponds to FCS of 0 to 21; "Borderline" to scores of 22 to 35, and

"Adequate" to scores of 35 and higher. Within the ZimVAC sample, FCS ranged from 0 to 119.

Table 12 shows that the percentage of households reporting adequate food consumption (scores

>= 35) decreased over the course of the drought (i.e., from 2014 to 2016), dropping from 63.9

percent in 2014 to 58.0 percent in 2015, then to 49.4 percent in 2016.

HDDS. Computation of HDDS follows guidelines developed by USAID36 and uses the same survey

questions as the FCS. It is a count of the number of food groups (out of 12) the household

consumed in the seven days prior to a survey. Table 12 shows that households maintained HDDS

through the first year of the drought but not the second. Households may have maintained HDDS,

even though adequate food consumption dropped, by substituting less nutritious foods or eating

nutritious foods less often.

Per capita daily expenditures are computed from households' reported purchases. Note that data

used to compute this measure are different than World Bank Living Standards Measurement Survey

(LSMS) data, (LSMS measures consumption and includes own production as a source) and so cannot

35 World Food Programme, Vulnerability Analysis and Mapping Branch (ODAV). 2008. Food consumption analysis: Calculation and

use of the food consumption score in food security analysis. Rome: WFP. 36 Swindale, Anne, and Paula Bilinsky. 2006. Household Dietary Diversity Score (HDDS) for Measurement of Household Food Access:

Indicator Guide (v.2). Washington, D.C.: FHI 360/FANTA.



be used to estimate the percentage of households with per capita daily expenditures less than

$1.25. Per capita daily expenditures dropped in the first year of the drought (2014-2015), from a

mean of $0.54 in 2014 to $0.46 in 2015 and dropped again in the second year of drought, to $0.41

in 2016.

Moderate to severe hunger is derived from the household hunger scale (HHS). HHS is computed

following methods developed by the USAID/Food and Nutrition Technical Assistance project

(FANTA).37 Data to compute the HHS come from four questions regarding the frequency of

household food insecurity over the 30 days prior to a survey. Based on the HHS which ranges from

0 to 6, households are categorized as experiencing little to no hunger (0 to 1), moderate hunger (2

to 3), or severe hunger (4 to 6). Households reporting moderate to severe hunger increased each

year of the drought, but more so in the second year. There was a 33 percent increase in

households reporting moderate to severe hunger between 2014 and 2015, and a 63 percent

increase between 2015 and 2016.

Recovery is the percentage of households in 2016 who reported exposure to a crop or livestock

shock and recovered to the same or better from all crop and livestock shocks experienced. Table

12 shows that only 4.4 percent of households reported recovery. Note that the percentage of

households reporting recovery is too low to use in an estimation equation.

Table 12: Well-being outcomes

n

2013 2014 2015 2016 2013 2014 2015 2016

Adequate food consumption (%) 45.0 a 63.9 a 58.0 a 49.4 a 1801 1799 1826 2364

HDDS (0-12, mean) 5.4 ab 6.0 ac 5.8 bd 5.4 cd 1793 1796 1811 2364

Per capita daily expenditures

(USD 2016, mean) 0.50 a 0.54 b 0.46 ab 0.41 ab 1801 1799 1823 2364

Moderate to severe hunger (%) 28.0 a 11.5 ac 16.0 bd 26.2 cd 1801 1799 1823 2330

Recover to same or better 4.4 2040



Findings: The percentage of households reporting adequate food consumption fell in both years of

the drought. HDDS and the percentage of households reporting moderate to severe hunger did not

drop until the second year of the drought. Per capita daily expenditures dropped in year one and

stayed below pre-drought levels through 2016. Almost no one reported any recovery.

37 Ballard,Terri; Coates, Jennifer; Swindale, Anne; and Deitchler, Megan. Household Hunger Scale: Indicator Definition and

Measurement Guide. Washington, DC: Food and Nutrition Technical Assistance II Project, FHI 360.

https://www.fantaproject.org/monitoring-and-evaluation/household-hunger-scale-hhs


RESULTS FROM MULTIVARIATE EQUATIONS COMBINING DATA OVER FOUR YEARS 26

4. Results from multivariate equations combining data over

four years

Sections that follow present results from a series of multivariate regression equations that are used

to estimate the relationships between multiple variables and outcomes. Regression equations test

the hypothesis that household resilience capacity buffers the negative effects of shocks on well-

being outcomes. Initial models included interaction terms equal to values of the drought variables

multiplied by household resilience capacity index score. Data provide some (albeit weak) evidence

that household resilience capacity mitigates the negative effects of shocks. Two equations (out of

30); one estimating HDDS using four years of combined data and one estimating per capita daily

expenditures using data from 2016 had significant coefficients on interaction terms. These results

indicate that household resilience capacity buffered the effects of shocks on HDDS and per capita

daily expenditures (in 2016). The effects were small 0.2 (on a 0 to 12 scale) change in HDDS as

conditions move from rainy to severe drought and less than one cent per day in per capita

expenditures for each additional crop or livestock shock. In the remaining 28 equations, interaction

terms were insignificant and/or caused coefficients on main terms (shocks and household resilience

capacity) to become insignificant. For those equations, a likelihood ratio test38 showed that the

equations omitting interaction terms better fit the data.

Results presented in this section are from analyses using a dataset that combines all four years of

household survey data (2013-2016). Combining four years of data allows estimates of the effects

over time of shocks, household resilience capacities, and other explanatory variables on coping

strategies and well-being outcomes. However, in order to combine four years of data into one

equation, the same variables need to be in all datasets. This results in simplified, or reduced-form

equations. Equations include as explanatory variables: the household resilience capacity index,

drought (from AFDM)39 as the measure of shock exposure, DFSA programming (a dummy variable

indicating the household is located in a designated DFSA ward), household demographic and

economic characteristics, year, and geographic controls.

Figure 3 through Figure 8 in this section show the relationship between household resilience

capacity, coping strategies, and well-being outcomes across three levels of drought. The figures

show predicted values of outcome variables that were computed using results from multivariate

regression equations. Predicted values for outcomes are at the 25th, 50th, 75th percentiles, and mean

values for the household resilience capacity index and 10th, 50th, and 90th percentile for rainfall. The

predictions hold all values of other explanatory variables constant at their means. Tables of results

are included in Appendix 1. The figures show changes in coping strategies and well-being outcomes

associated with increased household resilience capacity, moving along the horizontal (x) axis from

38 Greene, W.H. 1993. Econometric Analysis, Second Edition, New York: Macmillan Publishing Company 39 AFDM provide rainfall data for each year. Other shock exposure measures are limited to one or two years.



household resilience capacity scores at the 25th through the 50th (median) and to the 75th

percentile, as well as the mean. The vertical (y) axis shows three levels of rainfall. The change

associated with increased household resilience capacity is equal to the slope of the line(s). The

estimated impact of drought on coping strategies and well-being outcomes is equal to the y-

intercept. In all equations except per capita daily expenditures, household resilience capacity and

drought both were statistically significant (<0.05). In the equation estimating per capita food

expenditures, household resilience capacity was significant.

Coping strategies 2013-2016

CSI. Figure 3 shows predicted values of CSI across household resilience capacity index scores.

Coefficients from a censored (Tobit) regression equation are used to compute predicted values.

Complete results are in Table 15. Figure 3 compares the relationship between CSI and household

resilience capacity over three levels of drought exposure and shows that CSI scores decreases as

household resilience capacity increases. A decrease in the CSI of about 1.7 is associated with

moving from the 25th to 75th percentile on the household resilience capacity index. As household

resilience capacity increases, households' use of food coping strategies decreases. Shifting from non-

drought to drought conditions increases the CSI by about 17. As drought conditions worsen, use of

food coping strategies increases.

Figure 3: Results from regression equation (Tobit) estimating CSI, 2013-2016

Sources: ZimVAC household surveys 2013, 2014, 2015, 2016; AFDM, 2017.



Negative coping strategies. Figure 4 presents the changes in probability that a household engaged in a

negative coping strategy associated with changes in household resilience capacity and how the

relationship differs across drought conditions. (Complete regression results are presented in Table

15). Figure 4 shows that the probability of engaging in negative coping strategies decreases as

household resilience capacity increases. Moving from a household resilience capacity score in the

25th percentile to the 75th percentile is associated with a 0.01 drop in the probability that a

household will engage in a negative coping strategy. Moving from no drought to drought conditions

increases the probability that a household will engage in a negative coping strategy by almost 0.14.

Figure 4: Results from regression equation (logit) estimating negative coping

strategies, 2014-2016

Sources: ZimVAC household surveys 2014, 2015, 2016; AFDM, 2017

Well-being outcomes 2013-2016

The next series of figures presents results from multivariate regression equations estimating well-

being outcomes: adequate food consumption, HDDS, per capita daily expenditures, and moderate

to severe hunger. Complete regression equation results are presented in Table 15.

Adequate food consumption. Figure 5 shows the predicted probability of adequate food consumption

associated with changes in household resilience capacity across drought conditions. (see Appendix

1, Table 15). Figure 5 shows that the probability of adequate food consumption increases with

higher levels of household resilience capacity regardless of drought, rising by 0.07 as household

resilience capacity moves from 25th to 75th percentile.



Figure 5: Results from regression equations estimating adequate food

consumption, 2013-2016

Sources: ZimVAC household surveys 2013, 2014, 2015, 2016; AFDM, 2017

HDDS. Figure 6 shows results from an OLS regression equation estimating the relationship between

HDDS and household resilience capacity across different levels of drought. Complete results are

presented in Table 15 (Appendix 1). For HDDS, the estimation equation showed that household

resilience capacity has a larger effect on households during a drought, helping to mitigate the effects

of drought. Figure 6 shows that HDDS improves by 0.2 as households move from the 25th to 75th

percentile of household resilience capacity during non-drought conditions and by 0.4 during drought

conditions.



Figure 6: Results from regression equation (OLS) estimating HDDS, 2013-

2016

Non-overlapping CIs (red vertical lines) indicate statistically significant differences (<0.10).


Per capita daily expenditures. Figure 7 shows results from a generalized linear model (GLM)

estimating the relationship between household resilience capacity and per capita daily expenditures.

GLM is the general linear regression model of which OLS is a special case. GLM allows for response

variables that have error distributions other than a standard normal distribution. In this case, the

distribution of per capita daily expenditures is highly skewed (following a log linear rather than a

normal distribution). Accordingly, the estimation equation uses a log transformation. Complete

results are presented in Table 15 (Appendix 1). The data show that as households move from the

25th to 75th percentile in household resilience capacity, per capita daily expenditures increase by

about $0.06. Drought was not statistically significant in the estimation equation so is not displayed

in the figure.



Figure 7: Results from regression equation (GLM) estimating per capita daily

expenditures (USD 2016), 2013-2016


Moderate to severe hunger. Figure 8 shows results from a regression equation (logit) estimating the

relationship between household resilience capacity and the probability that a household

experienced moderate or severe hunger. Complete results are presented in Table 15 (Appendix 1).

Figure 8 shows that as household resilience capacity increases from the 25th to 75th percentile, the

probability of moderate to severe hunger drops by 0.04. Moving from non-drought to drought

conditions increases the probability of moderate to severe hunger by about 0.14.



Figure 8: Results from a regression equation (logit) estimating moderate to

severe hunger, 2013-2016


Findings: Higher levels of household resilience capacity are associated with better well-being

outcomes. For HDDS, household resilience capacity mitigates the effects of drought.

DFSAs and CSI

This section presents additional findings from analysis of the combined dataset (2013-2016)

(complete results are presented in Table 13). The four-year analysis does not have detailed

information on programming, so DFSA and non-DFSA dummy variables provide proxies for the

mix of respective programming. The figure shows that during the baseline (2013 and 2014) CSI

scores were higher (worse) for households in DFSA than in non-DFSA wards. This supports

DFSAs targeting households in poorer wards. In 2015, compared to 2014 (i.e., during the first

year of the drought), CSI scores increased significantly (worsened) in non-DFSA wards but

were not significantly different in DFSA wards (Figure 12). In 2016 CSI scores dropped from

2015 levels for households in both DFSA and non-DFSA wards, but were lower in DFSA than

non-DFSA wards, a switch from prior to programming. Over the four years CSI scores for

households in DFSA wards dropped by more than one-half. Descriptive data in Table 11 help to

interpret this finding. Increased access to FSN and improved water and sanitation in DFSA

wards may have contributed to lower CSI. In turn, other equations (Table 15) show that CSI is



a key determinate of adequate food consumption, HDDS, and per capita daily expenditures.

DFSA programming may be working indirectly on well-being outcomes by reducing CSI40.

Because multiple pair-wise comparisons are possible, Figure 12 includes vertical lines to show

90% confidence intervals (CIs) around estimates. The length of the vertical line (CI) shows the

range of the estimate. Shorter lines mean more precise estimates and narrower ranges. Longer

lines mean less precise estimates and wider ranges. Comparing across years and between DFSA

and non-DFSA wards; overlapping CIs mean the ranges overlap and there are no meaningfully

significant differences. Non-overlapping CIs show meaningfully significant differences (0.10).

Figure 9: Results from equation estimating CSI (Tobit), by DFSA and non-DFSA

wards

40 Note that CSI is not used in equations estimating the probability of moderate to severe hunger because questions for both

indictors are very similar.


RESULTS FROM EQUATIONS FOR 2013, 2014, 2015, AND 2016 34

5. Results from equations for 2013, 2014, 2015, and 2016

This section presents results from regression equations estimating coping strategies (CSI and

negative coping) and well-being outcomes (adequate food consumption, HDDS, per capita daily

expenditures, and moderate to severe hunger) separately for 2013, 2014, 2015, and 2016. Analysis

of datasets one year at a time provides additional information about shocks and household

resilience capacity. Year by year analysis provides more detailed information about factors

associated with coping strategies and well-being outcomes than are reported in the previous

section. Equations underlying results reported in this section include survey-specific measures of

shocks and NGO/government support, such as prices (2013, 2014, 2015), self-reported shocks

(2016), and crop and livestock assistance (2015 and 2016). These are in addition to the household

resilience capacity index, household demographic and economic characteristics, and geographic

control variables.

Price shocks

Price data are included in estimation equations for 2013, 2014, and 2015. Results (Table 16 through

Table 21) indicate that price shocks have negative impacts on household coping strategies and well-

being outcomes. Data from 2014 show that this is the case even in the absence of drought. Prices

may be picking up other information, such as access to markets, services, and infrastructure,

however they may also be an area where the government can assist by modifying pricing policies.

Overall, across coping strategies (CSI and negative coping) and well-being outcomes in each of the

four years (Table 16 to Table 23), analyses showed that increases in household resilience capacity

were associated with improvements in coping strategies and well-being outcomes. Exceptions were

per-capita income in 2013, and negative coping in 2014 and 2015.

Analyses using data from 2016 showed that household resilience capacity mitigates the effects of

crop and livestock shocks on per capita daily expenditures. Predicted values from the equation are

presented in Figure 10. The figure shows that as households move from the 25th percentile to the

75th percentile of household resilience capacity, per capita daily expenditures increase but the rate

of increase varies across shocks (slopes of the lines are different). For households who report no

shocks, per capita daily expenditures increase by $0.03 as households move from the 25th to 75th

percentile. For households reporting four shocks, per capita daily expenditures increase by by $0.07

as they move from the 25th to 75th percentile. The difference is just under $0.01 per shock. This

shows that as shock exposure increases, household resilience capacity mitigates the negative

impacts on per capita daily expenditures.



Figure 10: Results of equation estimating per capita daily expenditures

(USD2016) – shocks and household resilience capacity

Sources: ZimVAC. 2015. Household and community surveys, AFDM. 2017

NGO and government support

Figure 11 and Figure 12 that follow show results from equations estimating outcomes in 2015 and

2016 (Table 21 and Table 23) coinciding with the drought and after DFSAs were implemented. The

figures present predicted values of outcomes associated with changes in household resilience

capacity as well as NGO and government support. Intercepts (y-axis values) show predicted values

of outcomes associated with the type of support. The slope of each line is the change in the

predicted value of the outcome associated with scores on the household resilience capacity index,

moving from the 25th percentile, to the 50th percentile (median) and mean, and finally to the 75th

percentile. All variables reported in the figures met the 0.10 level of statistical significance.

Table 21 presents findings from analysis of the dataset from the 2015 household survey, which did

not include questions about shocks. Measures of shock exposure come from other secondary

sources: drought measured as rainfall (mm) (AFDM 2017) and goat prices as a measure of producer

prices (ZimVAC 2015 community survey). Figures 10a-d show predicted values from regression

equations estimating well-being outcomes for 2015. The figures show the association between

household resilience capacity and NGO/ government programming for different well-being

outcomes. The four graphs are presented together to show that increased household resilience

capacity is associated with improvements in all four well-being outcomes and to show the different

relationships between types of NGO/ government support and well-being outcomes.



Adequate food consumption. Figure 10a shows that higher levels of household resilience capacity are

associated with increased probability of adequate food consumption. Moving from the 25th to 75th

percentile on the household resilience capacity index increases the probability of adequate food

consumption by 0.11. Variables measuring NGO and government support did not meet the 0.10 cut

off for inclusion in the figure, suggesting different types of NGO or government support (as they

are measured in the survey) did not have any effect on adequate food consumption.

HDDS. Figure 11b shows that as household resilience capacity increases (e.g., moving from the 25th

to 75th percentile on the household resilience capacity index), HDDS increases by 0.2. Figure 10b

also shows that for any given level of household resilience capacity, households who receive both

FSN and agricultural and/or livestock assistance have higher HDDS. HDDS is 0.44 higher for

households who received agricultural and/or livestock assistance than household that did not

receive agricultural and/or livestock support, and 0.24 higher for households who received FSN

compared to those that did not. Households that received both types of support had an estimated

increase of almost 0.7 in HDDS compared to households receiving neither.

Per capita daily expenditures (USD 2016). Figure 10c shows that household resilience capacity is

associated with higher per capita daily expenditures, such that moving from the 25th to 75th

percentile is associated with an increase of $0.04. Households receiving agricultural and/or livestock

assistance show an estimated increase of about $0.19 in per capita daily expenditures. Per capita

daily expenditures of households receiving loans were $0.06 higher than households without loans.

Combining agricultural and livestock assistance and loans nearly doubled per capita daily

expenditures of households, increasing them by $0.25 compared to households receiving neither.

Moderate to severe hunger. Figure 10d shows that higher levels of household resilience capacity are

associated with lower probability of moderate to severe hunger. As households increase their

resilience capacity (i.e., moving from the 25th to 75th percentile on the household resilience capacity

index), there is an associated 0.01 reduction in the probability that a household will experience

moderate to severe hunger.


RESULTS FROM EQUATIONS FOR 2013, 2014, 2015 AND 2016 39

Figure 11: Results from equations measuring well-being outcomes, 2015

Figure 11a: Adequate food consumption (logit),

2015

Figure 11b: HDDS (OLS), 2015

Sources: ZimVAC. 2015. Household and community surveys,

AFDM. 2017


AFDM. 2017

Figure 11c: Per capita daily expenditures (GLM),

2015

Figure 11d: Moderate to severe hunger (logit),

2015


AFDM. 2017


AFDM. 2017

.



Figure 12 presents estimates of the relationship between household resilience capacity and

NGO/government support for the four well-being outcomes in 2016. Complete results are

presented in Table 23. The ZimVAC 2016 household survey included a shock exposure module

that provided the self-reported measures of shock exposure used in the equation. The measure is a

count of exposure to crop and/or livestock shocks ranging from 0 to 4 (Table 4) Rainfall (mm)

(AFDM) is the other shock variable. As with Figure 11, all results met the 0.10 level of statistical

significance.

Adequate food consumption. Figure 12a shows that higher levels of household resilience capacity are

associated with increased probability that a household has adequate food consumption. Moving

from the 25th to 75th percentile of household resilience capacity provides households with an

associated increase of 0.08 in the probability of adequate food consumption. Agricultural and/or

livestock assistance and FSN are both associated with increased probabilities of adequate food

consumption; agricultural and/or livestock assistance is associated with a 0.06 increase and FSN with

a 0.07 increase. Households receiving both are estimated to have an increase of 0.13 in the

probability of adequate food consumption, compared to households with neither.

HDDS. Figure 12b shows that as household resilience capacity moves from the 25th to 75th

percentile, HDDS increases by 0.2. Agriculture and/or livestock assistance is associated with an

increase of 0.2. HDDS scores for households with improved water and improved sanitation is 0.4

higher than for households with neither. The water and sanitation variable may be picking up effects

of access to other services (unmeasured in the survey), as households with improved water and

sanitation often have improved access to other infrastructure and services.

Per capita daily expenditures (USD 2016). Figure 12c shows that higher levels of household resilience

capacity are associated with higher per capita daily expenditures. Moving from the 25th to 75th

percentile on the household resilience capacity index increases per capita daily expenditures by

about $0.06. Agricultural and/or livestock assistance is associated with a $0.10 increase, loans with a

$0.03 increase. Per capita daily expenditure for households receiving loans and agricultural/livestock

assistance are estimated to be about $0.13 higher than households receiving neither.

Moderate to severe hunger. Figure 12d shows that increased household resilience capacity reduces

the probability that a household will experience moderate to severe hunger. As a household moves

from the 25th to 75th percentile in the household resilience capacity index, the probability of the

household experiencing moderate to severe hunger is reduced by 0.06. As in the analysis using 2015

data, agricultural and/or livestock assistance is associated with a 0.05 decrease in the probability of

household hunger.



Figure 12: Results from equations estimating well-being outcomes, 2016

Figure 12a: Adequate food consumption, 2016 Figure12b: HDDS, 2016

Sources: ZimVAC. 2016. Household survey, AFDM. 2017 Sources: ZimVAC. 2016. Household survey, AFDM. 2017

Figure12c: Per capita daily expenditures, 2016 Figure12d: Moderate to severe hunger, 2016

Sources: ZimVAC. 2016. Household survey, AFDM. 2017 Sources: ZimVAC. 2016. Household survey, AFDM. 2017



Of the programming variables, agriculture and livestock assistance was associated with improved

HDDS and higher per capita daily expenditures in both 2015 and 2016 and higher probability of

adequate food consumption in 2016, but paradoxically with increases in the probability of moderate

to severe hunger in both years. This seeming inconsistency could be measuring volatility in food

security conditions and imprecise measurement of programming. The agricultural and livestock

assistance measure covers the 2015-2016 agricultural season. Households reporting that they

received agriculture and livestock assistance may have received other programming (not measured

in the survey) as well. Household hunger covers the 30 days prior to a survey, whereas HDDS only

reflects seven days prior to a survey. Poorer households (more likely to experience moderate to

severe hunger) may have been targeted for support, then received support, in which case it might

be expected they would see improved food security after participating.

Elasticities

Figures in Appendix 3 present elasticities to compare the magnitude of change in dependent

variables for a one percent increase in explanatory variables. The figures show that in all four years,

a one percent increase in household resilience capacity was associated with increases in FCS and

HDDS of about 0.1 percent, increases in per capita daily expenditures of 0.2 percent, and decreases

of 0.1 percent in the probability that a household will experience moderate to severe hunger.

Findings from both the 2015 and 2016 show that agriculture and/or livestock assistance is

associated with improvements in adequate food consumption, HDDS, and per capita daily

expenditures, but has a negative association with moderate to severe hunger. This may be due to a

combination of programs targeting the poorest households, as well as the differences in survey

recall period for each outcome. Loan programs were associated with increased per capita

expenditures in both years. FSN was associated with higher HDDS and lower probability of

moderate to severe hunger in 2015 and with adequate food consumption in 2016.


SUMMARY 41

6. Summary

This study documents the detrimental effects of prolonged drought in four provinces of Zimbabwe.

The data cover 2013-2016: two years prior to the onset of the drought and two years during the

drought. Development Food Security Activities were implemented beginning in late 2014, after the

drought had already started. DFSA documents show – and household survey data corroborate – an

expansion in programming to include emergency activities as the drought progressed.

Households were able to maintain some assets through one year of drought but by the second

year, all assets were lower than – or at – pre-drought levels. CSI increased (worsened) in year one

of the drought but improved in year two. This is likely due to increased food, cash, and voucher

programs during the drought. Analysis of negative coping strategies shows that households deplete

savings and household assets first, then move to more negative strategies that can have longer-term

consequences (e.g., removing children from school).

Estimation equations provide some evidence that household resilience capacity mitigates the

negative impacts of shocks. Household resilience capacity increases HDDS by 0.2 (on a scale of 0-

12) as rainfall levels move from rainy to drought. Estimation equations for 2016 show that

household resilience capacity mitigates the negative impacts of crop and livestock shocks on per

capita daily expenditures. However, the effect is small. As the number of crop and livestock shocks

increases, household resilience capacity increases per capita daily expenditures by an additional

$0.01 per shock.

Even though programming was provided to households in both DFSA and non-DFSA wards, analysis

of the combined dataset (2013-2016) shows higher levels of improvement in CSI for households in

DFSA wards, indicating that the mix of programming in DFSA wards has lessened reliance on food

coping strategies.

Because the datasets are repeated cross-sections (independent samples) rather than a panel of the

same households over time, it is difficult to test whether programming in one time period increased

household resilience capacity and improved outcomes in later time periods. Additionally, data

indicate that in each cross-section (survey year) NGO and government programming have a

statistically significant relationship in both household resilience capacity (and its elements) and

outcomes. Because of this, it was not possible to estimate a simultaneous equation (two-stage) to

estimate the relationship between programming and resilience capacity (stage one), then between

resilience capacity and outcomes (stage two).


RECOMMENDATIONS 42

7. Recommendations

Policy related. Improved market access in non-drought times could possibly allow for de-stocking

prior to drought. However, macro-economic issues in Zimbabwe (cash shortages, livestock, and

cereal prices) limit the ability of markets to function properly. Regression analyses indicate that

household well-being outcomes are highly sensitive to price changes. Results show that higher

producer prices (goats) and lower consumer prices (maize and maize meal) could improve

outcomes.

Survey design. One of the objectives of this study was to use secondary data in a USAID/TANGO

resilience analytical framework. Some of the related research questions focus on the relationship

between programming and resilience. The survey design limits use of the data for detailed program

evaluation. The population-based survey (PBS) design is well suited to high level monitoring. A more

targeted sampling method could more accurately measure the relationship between programming,

household resilience capacity, and well-being outcome variables. Data showed that households in

DFSA and non-DFSA wards were receiving similar programming, so using non-DFSA wards as a

control group was not feasible. Working with DFSA partners to design a survey sample with both a

program group and a control group would improve the analysis. In addition, cross-sectional data

provide measures of association. A panel dataset is required to measure causation. Panel surveys do

not need large samples but have their own restrictions and requirements. Recurrent monitoring

surveys (RMS) are panel surveys that follow a subsample of households for a year and monitor their

responses to shocks and program uptake. RMS can provide detailed information about real-time

program utilization in the face of shocks, program timing and sequencing, as well as whether

program impacts endure after the program has ended.

Household survey. Minor changes would improve the ZimVAC household survey. Revising the shock

module from the 2016 ZimVAC household survey using information from the ZimVAC 2016

community survey and from shock modules used in other USAID/TANGO surveys would provide

better information about shock exposure. Module 9 (2016 survey) asks households to list and rank

sources of food and income. The information about sources can be extracted from the income

module and ranking of sources is not key information for resilience analysis. This module could be

eliminated, unless it is essential for other stakeholders. DFSA partners could also provide

suggestions for survey questions to include that would provide better measures of activity

participation. Existing questions on social capital in ZimVAC household surveys do not provide a

measure that is useful for resilience analysis. The social capital measure had a negative loading and

was dropped from the household resilience capacity index.

Community surveys. Redesigning the ZimVAC community survey would provide data to compute

transformative capacity and improve analyses. Other studies show that transformative capacity is

important for maintaining household well-being in the face of shocks. Some of the questions are

better suited to a focus group and/or key informant format using qualitative research methods. An


RECOMMENDATIONS 43

example is responses from community representatives about challenges, issues and constraints

related to livelihoods, inputs, markets, irrigation, and trade. Representatives could be interviewed as

a group using topical outlines and qualitative reporting methods. As they are currently collected

(using CSPro), data are stored as text fields, generating more than 55,000 different data points. (The

2015 community survey collects data from 880 communities and has 64 fields for information). Even

though the topics vary (livelihoods, inputs, markets, irrigation and trade), responses are similar

across topics and could be consolidated. Community data do provide information for updating the

household shocks and stresses module in the household survey. In 2015, community respondents

noted livestock and crop diseases and pests, wildlife destroying livestock, wildlife and livestock

destroying crops, veld fires, lack of inputs, high prices of inputs, late delivery of inputs as stressors.

These are in addition to shocks already included in the 2016 household survey. The community

survey also provides information about programming-related needs: inputs, market access,

veterinary care, draught power (tractor rental), and borehole repair. Finally, it provides information

about other issues that helps to understand how households are faring. These include difficulties

with border crossings into South Africa, encounters with police, non-payment by the Grain

Management Board (GMB), devaluation of the rand, and a severe cash shortage.

Price data were useful for this study. For the purpose of this study, data on all commodities are not

necessary. Also, data are incomplete across all commodities. However, complete information on

key consumer commodities and key producer commodities would be sufficient. If data collected by

WFP or other market surveys are representative, ZimVAC could use those data, saving on data

collection costs.


APPENDIX A: REGRESSION EQUATIONS FOUR YEARS COMBINED 44

Appendix A: Regression equations – four years combined

Table 13: Results from regression equation (Tobit) estimating CSI, 2013-2016

CSI

Household resilience capacity -0.318***

Rainfall (mm) -0.320***

DFSA Ward 7.922***

Survey year/2013

2014 -5.459**

2015 2.972

2016 -11.474***

Interaction DFSA ward & year/ DFSA 2013

DFSA 2014 -0.96

DFSA 2015 -13.549***

DFSA 2016 -16.119***

Household characteristics

Household size 1.305***

Gendered household type/male and female adults

Female headed, no adult males 0.463

Child headed, no adults 6.187

Male headed, no adult females -1.641

Education hh head -0.456

Age hh head -0.806*

Livelihood risk profile/No regular livelihoods

Climate -1.518

Econ-Salary -16.184***

Econ-Wages and trade 0.501

Climate-econ 1.145

Mining 4.17

Wealth tercile/Lowest tercile

Middle tercile -6.027***

Highest tercile -12.580***

Province/Manicaland

Matabeleland North 0.412

Matabeleland South -7.128***

Masvingo 6.716***

Constant 40.321***

sigma 35.748***

Observations 7643

Log likelihood -29799.347 a1986 left-censored observations at CSI=0

Sources: ZimVAC household surveys, 2013-2016



Table 14: Results from regression equation (logit) estimating negative coping

strategies 2014-2016

Negative coping (logit) Coefficient

Household resilience capacity -0.018**

Shock measure

Rainfall (mm) 0.015***

DFSA Ward -0.122

Survey year/2014

2015 0.097

2016 -0.095


Household size 0.133***

Gendered hh type/male and female adults

Female headed, no adult males 0.119

Child headed, no adults 0.537

Male headed, no adult females 0.166


Age hh head -0.006**

Livelihood risk profile/No regular livelihoods

Climate 0.442***

Econ-Salary -0.746***

Econ-Wages and trade 0.366***

Climate-econ 0.658***

Mining 0.237

Wealth tercile/Lowest tercile

Middle tercile -0.190**

Highest tercile -0.317***

Province/Manicaland -0.018**

Matabeleland North -0.589***

Matabeleland South -0.809***

Masvingo -0.459***

Constant -0.775*

Observations 5925

Log likelihood -2287.673

Asterisks denote levels of statistical significance: *<0.10, **<0.05, ***<0.01

Sources: ZimVAC. 2013, 2014, 2015, 2016. Household surveys. AFDM 2017.



Table 15: Results from regression equations estimating well-being outcomes over four years Adequate food

consumption (logit) HDDS (OLS) Per capita daily

expenditures (USD2016) (GLM)

Moderate to severe hunger (logit)

Household resilience capacity Drought - rainfall in past 6 mo. (mm) HH resilience capacity * drought DFSA wards Survey year/2013

2014 2015 2016

CSI Household characteristics

Household size Gendered household type/male and female adults

Female headed, no adult males Child headed, no adults Male headed, no adult females

Education hh head Age hh head

Livelihood risk profile/No regular livelihoods Climate Econ-Salary Econ-Wages and trade Climate-econ Mining

Wealth tercile/Lowest tercile Middle tercile Highest tercile

Province/Manicaland Matabeleland North Matabeleland South Masvingo

Constant

0.039*** -0.009***

-0.192***

-0.203* 0.343*** -0.065 -0.013***

-0.050***

-0.139** -0.167 -0.038 0.137*** 0.004**

0.260*** 0.313** 0.046 0.164** 0.420

0.561*** 0.875***

0.054 0.513*** 0.550*** -1.398***

0.055*** -0.024*** 0.000*** -0.146***

-0.730*** 0.063 -0.128** -0.010***

-0.056***

-0.047 -0.105 -0.151 0.109*** 0.002

0.227*** 0.427*** 0.008 0.208*** -0.058

0.449*** 0.750***

0.118 0.779*** 0.540*** 3.609***

0.020*** -0.001

-0.053

-0.402*** -0.139 -0.306*** -0.003***

-0.198***

-0.020 0.094 0.109 0.142*** 0.000

0.205*** 0.436*** 0.156*** 0.260*** -0.236

0.291*** 0.395***

-0.071 0.298*** -0.001 -0.595***

-0.034*** 0.012***

-0.032

-0.217 -0.464*** 0.036

0.087***

-0.033 -0.058 0.033 -0.103*** 0.003

0.204** -0.556*** 0.118 0.162* 0.089

-0.638*** -0.949***

0.368*** 0.017 -0.185** -0.275

Observations 7643 7620 7643 7672 r2 Log likelihood

-4668.648 0.204 -14730.346

-5289.874

-3588.712 Asterisks denote levels of statistical significance: *<0.10, **<0.05, ***<0.01Sources: ZimVAC. 2013, 2014, 2015, 2016. Household surveys. AFDM 2017.


APPENDIX B: REGRESSION EQUATIONS – YEAR BY YEAR 47

Appendix B: Regression equations – year by year

Table 16: Results from regression equation (Tobit) estimating CSI, 2013

CSI (Tobit)

Household resilience capacity -0.379**

Shocks

Maize price 350.997***

Drought-self reported 2.832

DFSA wards -2.520

NGO/Government support

Water/sanitation -2.650**

FSN -0.504

Cash transfers 0.058

Remittances -4.667*


Household size -0.295

Gendered household type/M&F adult households

Female headed, no adult males -0.147

Child headed, no adults -20.043*

Male headed, no adult females 3.319


Age hh head 0.058

Livelihood risk/No regular livelihoods

Climate -4.143

Econ-Salary 0.858

Econ-Wages and trade -2.748

Climate-econ -6.445**

Mining 12.783

Asset terciles/Lowest tercile

Middle tercile 1.703

Highest tercile 4.302

Province/Manicaland

Matabeleland North 46.098***

Matabeleland South 16.495***

Masvingo 20.946***

Constant -111.973***

sigma 33.353***

Observations 1477

Log likelihood -6637.39 a161 left censored observations at CSI<0


Sources: ZimVAC household surveys 2013, WFP 2017



Table 17: Results from regression equations estimating well-being outcomes, 2013

p(Adequate

food

consumption)

(logit)

HDDS (OLS)

Per capita

daily

expenditures

(USD2016)

GLM)

p(Moderate to

severe

hunger)

(Logit)

Household resilience capacity 0.057*** 0.059*** 0.013 -0.068***

Shocks

Maize price -13.454** -13.266*** 1.395 10.141*

Drought-self reported -0.006*** -0.005*** 0 0.004***

DFSA wards -0.436*** -0.136 -0.254*** 0.305**

NGO/Government support 0 0 0 0

Water/sanitation 0.009 0.047 0.072 -0.043

Formal safety nets 0.193 0.092 -0.019 0.019

Transfer -0.39 -0.856*** -0.097 0.787***

Remittances 0.478*** 0.597*** 0.136 -0.06

Coping strategies

CSI -0.003* -0.003** -0.003


Household size -0.025 -0.025 -0.222*** 0.103***

Female headed, no adult males -0.207 -0.019 0.093 0.191

Child headed, no adults -0.549 -0.356 -0.331 -0.925

Male headed, no adult females 0.033 -0.261 0.328 -0.124

Education hh head 0.044 0.055 0.319*** -0.026

Age hh head -0.002 -0.003 0.003 0.004

Livelihood risk/No regular

livelihoods 0 0 0 0

Climate 0.330* 0.522*** 0.451** -0.231

Econ-Salary 0.679* 0.222 1.010*** -0.321

Econ-Wages and trade -0.051 -0.039 0.592** -0.02

Climate-econ 0.059 0.216 0.578*** 0.096

Mining -0.617 -0.08 0.510** 0.492

Asset terciles/Lowest tercile 0 0 0 0

Middle tercile 0.539*** 0.416*** 0.427** -0.680***

Highest tercile 0.493** 0.441*** 0.435* -0.636**

Province/Manicaland 0 0 0 0

Matabeleland North -0.437* -1.042*** 0.245 1.508***

Matabeleland South -0.027 0.334* 0.441** 0.277

Masvingo -0.259 -0.369*** -0.144 0.350*

Constant 5.078** 10.389*** -2.108 -5.657**

Observations 1477 1473 1477 1537

r2 0.2

Log likelihood -920.243 -2833.283 -1063.756 -810.442


Sources: ZimVAC. 2013.. Household survey. AFDM 2017. WFP. 2017.



Table 18: Results from regression equations estimating coping strategies, 2014

CSI (Tobit) Negative coping

(logit)

Household resilience capacity -0.339*** -0.009

Shocks

Maize meal price -1.499 -1.000

Drought-self reported 0.931 -0.084

DFSA wards 6.881*** 0.338


Water/sanitation -3.924*** -0.375***

Cash transfers -0.185** -0.038*

Loans 4.752** 0.908***

Remittances -1.052 -0.065


Household size 1.311*** 0.106***


Female headed, no adult males -0.862 0.207

Child headed, no adults 19.365** 1.216

Male headed, no adult females 0.183 -1.094

Education hh head -1.251* -0.137

Age hh head -0.011 -0.012**


Climate -0.861 0.656**

Econ-Salary -7.732** -1.632

Econ-Wages and trade 4.123** 0.477*

Climate-econ 5.892** 0.584*

Mining 1.978 0.000


Middle tercile -6.640*** 0.228

Highest tercile -9.953*** -0.110

Province/Manicaland

Matabeleland North -0.055 0.222

Matabeleland South 8.055*** 0.493

Masvingo 3.041 0.689***

Constant 14.654** -1.376

Sigma 25.835***

Observations 1671 1663

Log likelihood -5820.054 -463.987

a509 left censored observations at CSI<0


Sources: ZimVAC household and community surveys 2014



Table 19: Results from regression equations estimating well-being outcomes, 2014

p(Adequate

food

consumption)

(logit)

HDDS (OLS)

Per capita

daily

expenditures

(USD2016)

GLM)

p(Moderate to

severe

hunger)

(logit)

Household resilience capacity 0.033*** 0.035*** 0.017*** -0.030*

Shocks Maize meal price -1.477*** -1.477*** -2.143** 2.538***

Drought-self reported -0.009 -0.009 -0.258*** 0.327*

Goat prices -0.002 -0.002 -0.009 -0.037**

DFSA wards -0.219 -0.266*** -0.142 0.043

NGO/Government support Water/sanitation 0.034 0.030 0.136** -0.164

Cash transfers -0.030 0.003 0.004 -0.036**

Loans 0.347 -0.101 -0.255 0.943***

Remittances 0.286* 0.376*** -0.035 -0.201

Coping strategies -0.028*** CSI -0.028*** -0.032 -0.005 Negative coping -0.032 -0.037 0.011 1.076***

Household characteristics Household size -0.037 -0.059*** -0.166*** 0.023


Female headed, no adult males -0.373*** -0.155 0.064 -0.301

Child headed, no adults -0.478 0.116 0.086 0.294

Male headed, no adult females 0.676** -0.187 -0.083 -0.241

Education hh head 0.067 0.019 0.146*** -0.114

Age hh head 0.011*** 0.004 -0.002 -0.036**


Climate 0.540*** 0.166 0.141 -0.201

Econ-Salary 0.230 0.343 0.500** -0.700

Econ-Wages and trade 0.110 -0.026 0.088 -0.108

Climate-econ 0.496** 0.179 0.171 -0.126

Mining -0.254 -0.608 -0.351 -0.049

Asset terciles/Lowest tercile Middle tercile 0.368** 0.371*** 0.195 -0.849***

Highest tercile 0.624*** 0.709*** 0.324* -1.160***

Province/Manicaland Matabeleland North 0.418* 0.334* 0.072 -0.292

Matabeleland South 1.088*** 0.621*** 0.425*** 0.069

Masvingo 0.612*** 0.667*** 0.103 -0.270

Constant 0.395 5.679*** 0.942 -1.681**

Observations 1671 1668 1671 1671

r2 0.245 Log likelihood -924.415 -3172.320 -1434.976 -499.561






CSI (Tobit)

Negative coping

(logit)

Household resilience capacity -0.975*** -0.016

Shocks

Drought (rainfall mm) -0.114 -0.017***

Maize meal price -14.468*** 0.312*

Goat prices -0.369*** 0.013

DFSA wards -0.114 -0.088


FSN -1.606 -0.243

Ag/Livestock support 3.475** 0.476***

Cash transfers 5.605 -0.784

Loans 5.740** 0.186

Remittances 2.414 0.066



Gendered household type/M&F adults

Female headed, no adult males -0.262 0.164

Male headed, no adult females 0.992 0.700**

Education hh head -0.574 -0.035

Age hh head 0.077 -0.008*


Climate 3.860 0.646***

Econ-Salary -18.959*** -0.107

Econ-Wages and trade 4.334** 0.366*

Climate-econ 7.455*** 0.730***

Mining 0.609 0.787

Asset terciles/Lowest tercile 0.000 0.000

Middle tercile -7.909*** -0.546***

Highest tercile -15.218*** -0.645***

Province/Manicaland

Matabeleland North -13.620*** -2.079***

Matabeleland South -3.302 -1.439***

Masvingo -21.519*** -1.275***

Constant 40.641*** -1.284*

Sigma 31.065*** 1794

Observations 1794 Log likelihood -7543.874 -691.181

a291 left censored observations at CSI<0. Asterisks denote levels of statistical significance: *<0.10, **<0.05, ***<0.01

Sources: ZimVAC household and community surveys 2015, AFDM 2017



Table 21: Results from equations estimating well-being outcomes, 2015 Adequate

food

consumption

(logit)

HDDS

(OLS)

Per capita

daily

expenditures

(USD2016)

(GLM)

(Moderate

to severe

hunger)

(Logit)

Household resilience capacity 0.076*** 0.032*** 0.021*** -0.047**

Shocks

Drought -0.015*** -0.011*** 0.004 0.011*

Goat prices 0.007 0.021*** 0.020*** 0.021*

DFSA wards 0.068 -0.028 0.036 -0.155

Coping strategies

CSI -0.011*** -0.012*** -0.006***

Negative coping -0.186* -0.271*** -0.047 0.634***

NGO/government support

Formal safety nets 0.149 0.229*** -0.005 -0.241

Ag/livestock support 0.100 0.443*** 0.191** 0.367**

Transfers 0.006 0.002 0.001 -0.003

Loan 0.310 0.521*** 0.498*** 0.000

Remittances 0.566*** 0.173 0.218** 0.265


Household size -0.080*** -0.084*** -0.244*** 0.116***

Female headed household -0.202 -0.139 -0.315*** -0.136

Male headed household 0.092 -0.353* 0.009 0.508

Education hh head 0.081 0.077** 0.043 -0.020

Age hh he 0.001 -0.003 -0.004 0.004


Climate 0.256 -0.031 0.204* 0.254

Econ-Salary 0.387 0.528*** 0.491*** -0.574

Econ-Wages and trade 0.199 0.107 0.054 0.288

Climate-econ 0.271 0.105 0.186 0.505**

Mining 1.315* 0.074 -0.455* -0.348


Middle tercile 0.623*** 0.255*** 0.344*** -0.602***

Highest tercile 1.002*** 0.822*** 0.504*** -1.289***

Province/Manicaland

Matabeleland North 0.357 -0.494** -0.972*** -0.672**

Matabeleland South 0.663** -0.114 -0.240 -0.999***

Masvingo 0.470*** 0.129 -0.230* -1.466***

Constant -1.900*** 4.450*** -0.386 -1.381**

Observations 1794 1782 1794 1794

r2 0.233

Log likelihood -1049.478 -3347.347 -994.450 -689.569


Sources: ZimVAC household and community surveys 2015, AFDM 2017




CSI (Tobit) Negative coping

(logit)

Household resilience capacity -0.399* -0.023*

Shocks

Drought (rainfall mm) 0.380*** 0.043***

Crop and/or livestock shock 3.995*** 0.160**

DFSA wards -0.948 -0.223*


Water/sanitation -3.459** -0.092

FSN -2.699 -0.012

Ag/Livestock support 1.290 -0.037

Cash transfers 13.368*** 0.360*

Loans -0.660 0.187

Remittances -3.459** -0.092



Gendered household type/M&F adults

Female headed, no adult males 1.415 -0.010

Male headed, no adult females -6.150 0.203

Education hh head -2.885*** -0.057

Age hh head -0.025 0.000


Climate -2.581 0.286

Econ-Salary -23.043*** -1.055**

Econ-Wages and trade 2.037 0.341**

Climate-econ 4.462 0.687***

Mining 14.804 0.000


Middle tercile -11.369*** -0.079

Highest tercile -20.318*** -0.189

Province/Manicaland

Matabeleland North 4.075 -0.287

Matabeleland South -15.121*** -1.229***

Masvingo 29.189*** -0.727***

Constant 24.668*** 0.157

Sigma 40.558***

Observations 2353a 2350

Log likelihood -7743.318 -1001.606

a958 left censored observations at CSI<0


Sources: ZimVAC 2016, AFDM 2017



Table 23: Results of equations estimating well-being outcomes, 2016

p(Adequate

food

consumption

) (logit)

HDDS

(OLS)

Per capita

daily

expenditures

(USD2016)

(GLM)

(Moderate

to severe

hunger)

(logit

Household resilience capacity 0.046*** 0.032*** 0.016*** -0.054***

Shocks

Drought -0.008 -0.032*** -0.003 0.053***

Crop and/or livestock shocks -0.137*** -0.179*** -0.066* 0.222***

HH resilience capacity * Crop and/or

livestock shocks 0.003**

DFSA wards -0.239** -0.188** 0.047 -0.128


Water/sanitation 0.206*** 0.206*** 0.115*** -0.031

Formal safety nets 0.270*** 0.091 -0.157*** -0.144

Ag/livestock support 0.239** 0.193*** 0.091* 0.248**

Loan 0.051 0.189 0.280*** 0.261

Remittances 0.433 0.437** 0.285** 0.383

Coping Strategies

CSI -0.017*** -0.009*** -0.005***

Negative coping -0.204** -0.185*** 0.066 1.098***


Household size -0.050** -0.043*** -0.204*** 0.045*

Gendered HH type/male & female adults

Female headed HH, no adult males 0.004 -0.035 0.000 -0.201

Male headed household, no adult females 0.304 0.088 0.186* -0.030

Education hh head 0.197*** 0.139*** 0.102*** -0.150***

Age hh head 0.001 0.002 0.002 0.003


Climate 0.051 0.330*** -0.032 0.395**

Econ-Salary -0.122 0.346** 0.151** -0.139

Econ-Wages and trade 0.174 0.105 0.024 0.009

Climate-econ 0.011 0.439*** 0.182* -0.093

Mining 0.000 0.733 -0.065 -0.033

Wealth terciles/Lowest tercile

Middle tercile 0.461*** 0.587*** 0.153 -0.651***

Highest tercile 0.831*** 0.784*** 0.295*** -0.914***

Province/Manicaland

Matabeleland North -0.081 0.101 0.100 0.304

Matabeleland South 0.660*** 0.820*** 0.299*** 0.118

Masvingo 1.233*** 0.908*** 0.161** -0.263

Constant -1.777*** 2.815*** -0.898*** 1.140**

Observations 2349 2353 2353 2320

r2 0.214

Log likelihood -1405.569 -4501.314 -567.716 -1131.334




APPENDIX C: THE HOUSEHOLD RESILIENCE CAPACITY INDEX 55

AppendixC:Thehouseholdresiliencecapacityindex

The household resilience capacity index was constructed separately for each year 2013, 2014, 2015, and 2016. The index is made up of up to eight elements, some of which are themselves indexes. The elements corresponding to each year are as follows:

Table 24: Household resilience capacity elements

2013 2014 2015 2016

Cereal stores (USD 2016) √ √ √ √ Livestock (TLU) √ √ √ √

Education level head of household √ √

Adults in HH with more than primary level education √ √ Savings √ √ √ √ Livelihood diversification √ √ √ √ Remittances as an income source √

ISN1

Social capital1 Agricultural markets √ √ √ Livestock markets √ √ Information sources √

1Variables had negative loadings, so were dropped from the final factor

The cereal stores index replaces household assets41 which is used in other USAID/TANGO studies because data on household and productive assets were not collected in the ZimVAC household surveys. The ZimVAC surveys asked households about food stocks (cereal and pulses) on hand as of April 1 of the survey year. Respondents were asked about quantity and units of maize, sorghum, millet, wheat, rice ground nuts (shelled and unshelled), round nuts (shelled and unshelled), chick peas, and beans. Computation entailed converting units to kilograms and then attaching prices to each commodity using WFP price per kilogram. All values were then summed and converted to USD 2016 to be comparable across years.

Livestock assets were computed according to USAID/TANGO methods, which follow FAO guidelines42. The index is a count of each type of livestock multiplied by its TLU. Subtotals are summed to create the index.

Education is measured in two ways. The 2013 and 2014 surveys ask about education of the head of household only, not all household members. For those years, that is the education element. For 2015 and 2016 education is the sum of adults in the households (18 and older) with more than a primary level education.

41 Household assets is a count of approximately 25 household and approximately 20 productive assets. 42 Food and Agriculture Organization (FAO). 2011. Guidelines for the preparation of livestock sector reviews.


APPENDIX C: THE HOUSEHOLD RESILIENCE CAPACITY INDEX 56

Savings is the dollar amount of savings. It comes from an income module asking about

income from various sources over the past month. Savings is equal to what households

reported as the starting balance.

Livelihood diversification also comes from the income module. A household is counted as having an

income source if they report cash or in-kind earnings from that source. Income sources are

grouped into seven livelihood categories then totaled.

1. Sale of livestock, livestock products or draught animal hiring=livestock

2. Food crops, cash crops or vegetables=crops

3. Formal employment or own business (if own business earned more than $500)=formal

4. Casual labor=casual labor

5. Cross border trade, brewing, petty trade, artisanal trade and own business (if own business

earned less than $500 in past month)=trade

6. Small scale mining=mining

7. Gathering=wild products

Remittances as an income source also comes from the income module. It is the dollar amount or

dollar equivalent of cash or in-kind from remittances in the past month.

ISN is a count (0-5) of cash, food, agriculture, livestock or wash inputs household received from

churches over the past 12 months. It comes from the social protection module.

Social capital also comes from the social protection module. It is a count (0-10) of cash, food,

agriculture, livestock or wash inputs household received from friends and/or relatives in rural areas

or friends and/or relatives in urban areas over the past 12 months.

Agricultural markets. This is a dummy variable, coded as one if the household sold agricultural

products to traders, GMB, millers, markets, or contractors, and zero otherwise.

Livestock markets. This is a dummy variable, coded as one if the household sold agricultural products

to traders, CSC, other abattoirs or distant markets and zero otherwise.

Information sources. Is a count (0-10) of whether a household received information on the following

topics from NGOs, government, newspaper, radio/TV or Internet/SMS:

1. Long term changes in weather patterns

2. Rainfall prospects for the season just ended

3. Livestock disease threats

4. Current market prices for live animals in the area

5. Business and investment opportunities

6. Opportunities for borrowing money

7. Market prices of food that you buy

8. Market prices of agricultural inputs and veterinary supplies

9. Child feeding and caring practices

10. Health information


APPENDIX D: RELATIONSHIPS BETWEEN HOUSEHOLD RESILIENCE CAPACITY ELEMENTS AND WELL-BEING OUTCOMES 57

Appendix D: Relationships between household resilience capacity elements and well-

being outcomes 2013, 2014, 2015, 2016

Table 25: Relationships between elements of household resilience capacity & adequate food consumption 2013

p(Adequate food consumption) (logit) 2013

Shocks

Maize price -13.803*** -14.282*** -12.252** -11.910** -13.470** -13.454**

Drought - self reported -0.005*** -0.005*** -0.006*** -0.006*** -0.006*** -0.006***

Resilience capacity elements

Cereal stores 0.001**

Livestock assets

0.055***

Count of livelihoods

0.233*

Education HH head

0.150***

Savings

0.006***

HH resilience capacity

0.057***

DFSA wards -0.361*** -0.404*** -0.409*** -0.427*** -0.438*** -0.436***

CSI -0.004** -0.003* -0.004** -0.004** -0.003* -0.003*


Household size -0.006 -0.013 -0.026 -0.025 -0.029 -0.025

Female headed household -0.274** -0.231* -0.235* -0.227 -0.231 -0.207

Child headed household -0.760 -0.765 -0.629 -0.605 -0.555 -0.549

Male headed household 0.017 -0.016 0.052 0.057 0.038 0.033

Education HH head 0.167*** 0.149*** 0.145***

0.102* 0.044

Age HH head 0.003 0.001 -0.001 -0.001 -0.002 -0.002

Livelihood risk category/No regular livelihoods

Climate 0.403** 0.423** 0.136 0.394** 0.435** 0.330*

Econ-Salary 0.701* 0.770** 0.523 0.807** 0.771** 0.679*

Econ-Wages & trade -0.067 -0.046 -0.266 0.012 0.055 -0.051

Climate-econ 0.172 0.223 -0.369 0.165 0.230 0.059

Mining -0.501 -0.424 -0.747 -0.481 -0.702 -0.617


Middle tercile

0.691*** 0.694*** 0.617*** 0.539***




Highest tercile

1.031*** 1.045*** 0.866*** 0.493**

NGO/government programming

Water/sanitation -0.004 -0.014 0.010 0.010 0.021 0.009

FSN 0.113 0.107 0.143 0.136 0.218* 0.193

Cash or in-kind transfer -0.364 -0.361 -0.358 -0.389 -0.415 -0.390

Remittances 0.494*** 0.513*** 0.273 0.500*** 0.455*** 0.478***

Province/Manicaland

Matabeleland North -0.337 -0.381 -0.411 -0.417* -0.403 -0.437*

Matabeleland South 0.108 0.060 0.036 0.035 -0.035 -0.027

Masvingo -0.198 -0.157 -0.223 -0.217 -0.261 -0.259

Constant 5.087*** 5.406*** 4.501** 4.372** 4.994** 5.078**

Observations 1477 1477 1477 1477 1477 1477

Log likelihood -950.440 -947.007 -927.765 -929.331 -913.549 -920.24

Asterisks denote levels of statistical significance: * p<0.10, ** p<0.05, *** p<0.01

Sources: ZimVAC. 2013. Household surveys, WFP. 2017.



Table 26: Relationships between elements of household resilience capacity and HDDS 2013

HDDS (OLS) 2013

Shocks

Maize price -13.964*** -14.094*** -11.766*** -11.386*** -12.712*** -13.266***

Drought - self-reported -0.005*** -0.005*** -0.006*** -0.005*** -0.005*** -0.005***


Cereal stores 0.001***

Livestock assets

0.070***


0.290***

Education HH head

0.163***

Savings

0.005***


0.059***

DFSA wards -0.071 -0.132 -0.102 -0.125 -0.135 -0.136

CSI -0.004*** -0.003** -0.004*** -0.004*** -0.004** -0.003**


Household size -0.008 -0.017 -0.027 -0.026 -0.028 -0.025

Female headed household -0.105 -0.041 -0.058 -0.049 -0.042 -0.019


Male headed household -0.289 -0.324 -0.253 -0.246 -0.253 -0.261


0.129*** 0.055

Age HH head 0.002 -0.001 -0.002 -0.002 -0.003 -0.003

Livelihood risk category/No regular

Climate 0.601*** 0.627*** 0.276 0.597*** 0.621*** 0.522***

Econ-Salary 0.311 0.312 0.036 0.401 0.365 0.222

Econ-Wages & trade -0.060 -0.033 -0.326* 0.019 0.060 -0.039

Climate-econ 0.326** 0.383*** -0.345 0.316** 0.374*** 0.216

Mining

0.092 -0.334 -0.002 -0.073 -0.080


Middle tercile

0.574*** 0.580*** 0.518*** 0.416***

Highest tercile

1.031*** 1.055*** 0.889*** 0.441***


Water/sanitation 0.042 0.027 0.051 0.051 0.058 0.047

FSN 0.005 -0.001 0.027 0.020 0.098 0.092



HDDS (OLS) 2013

Cash or in-kind transfer -0.856*** -0.849*** -0.831*** -0.868*** -0.878*** -0.856***

Remittances 0.640*** 0.658*** 0.345** 0.631*** 0.584*** 0.597***

Province/Manicaland

Matabeleland North -0.947*** -1.012*** -1.012*** -1.021*** -1.012*** -1.042***

Matabeleland South 0.497*** 0.413** 0.409** 0.410** 0.347** 0.334*

Masvingo -0.306** -0.268** -0.324** -0.316** -0.361*** -0.369***

Constant 10.493*** 10.711*** 9.706*** 9.561*** 10.081*** 10.389***

Observations 1473 1473 1473 1473 1473 1473

r2 0.144 0.161 0.180 0.175 0.198 0.200

Log likelihood -2883.117 -2868.632 -2852.069 -2855.987 -2835.661 -2833.283





Table 27: Relationships between elements of household resilience capacity & per capita daily expenditures 2013

Per capita daily expenditures (USD2016) (GLM) 2013

Shocks

Maize price 2.600 1.896 1.948 3.487 0.359 0.083

Drought - self-reported -0.000 0.000 -0.001 0.000 0.000 0.000


Cereal stores 0.000**

Livestock assets

0.010


0.259***

Education HH head

0.346***

Savings

0.002***


0.014

DFSA wards -0.115 -0.143* -0.216** -0.206** -0.285*** -0.271***

CSI -0.004* -0.003 -0.003* -0.003* -0.003 -0.003


Household size -0.195*** -0.206*** -0.211*** -0.218*** -0.218*** -0.225***

Female headed household 0.084 0.098 0.095 0.070 0.082 0.104


Male headed household 0.342 0.360 0.367* 0.319 0.313 0.343


0.307*** 0.314***

Age HH head 0.004 0.005 0.003 0.001 0.001 0.003


Climate 0.458*** 0.489** 0.141 0.422** 0.442** 0.454***

Econ-Salary 1.094*** 1.031*** 0.759*** 1.116*** 1.038*** 0.997***

Econ-Wages & trade 0.622** 0.653** 0.322 0.610** 0.548** 0.571**

Climate-econ 0.666*** 0.710***

0.589*** 0.543*** 0.562***

Mining 0.481 0.504* 0.310 0.555** 0.427* 0.483*


Middle tercile

0.424*** 0.460*** 0.439** 0.443**

Highest tercile

0.493*** 0.599*** 0.548*** 0.427*


Water/sanitation 0.079 0.050

0.081

FSN 0.036 0.004

0.012




Cash or in-kind transfer -0.085 -0.108

-0.084

Remittances 0.261 0.234

0.177

Province/Manicaland

Matabeleland North 0.382* 0.356 0.412** 0.289 0.237 0.242

Matabeleland South 0.585** 0.552** 0.612*** 0.489** 0.407** 0.478**

Masvingo -0.082 -0.086 -0.186 -0.131 -0.140 -0.138

Constant -2.792 -2.510 -2.383 -2.922* -1.442 -1.475

Observations 1477 1477 1477 1477 1477 1477

Log likelihood -1123.707 -1118.613 -1047.813 -1071.325 -1054.371 -1068.71





Table 28: Relationships between resilience capacity & moderate to severe hunger 2013

p(moderate to severe hunger) (logit) 2013

Shocks

Maize price 10.365* 12.381** 9.193 9.124 9.555 10.141*

Drought - self-reported 0.003** 0.004*** 0.004*** 0.004*** 0.004*** 0.004***


Cereal stores -0.003***

Livestock assets -0.148***

Count of livelihoods -0.091

Education HH head -0.148**

Savings -0.002

HH resilience capacity -0.068***

DFSA wards 0.222 0.323** 0.292* 0.300** 0.305** 0.305**

CSI


Household size 0.075*** 0.096*** 0.101*** 0.100*** 0.102*** 0.103***

Female headed household 0.236 0.157 0.196 0.194 0.198 0.191


Male headed household -0.063 -0.070 -0.143 -0.144 -0.134 -0.124

Education HH head -0.172*** -0.124* -0.146** -0.133** -0.026

Age HH head -0.001 0.003 0.003 0.003 0.003 0.004


Climate -0.347* -0.334* -0.201 -0.303 -0.311 -0.231

Econ-Salary -0.332 -0.478 -0.347 -0.461 -0.441 -0.321

Econ-Wages & trade -0.038 -0.043 0.007 -0.100 -0.112 -0.020

Climate-econ -0.059 -0.100 0.162 -0.046 -0.061 0.096

Mining 0.411 0.337 0.476 0.374 0.407 0.492


Middle tercile -0.86*** -0.86*** -0.83*** -0.680***

Highest tercile -1.25*** -1.25*** -1.18*** -0.636**


Water/sanitation -0.025 -0.015 -0.041 -0.041 -0.044 -0.043

FSN 0.094 0.090 0.065 0.066 0.041 0.019



Table 28: Relationships between resilience capacity & moderate to severe hunger 2013

p(moderate to severe hunger) (logit) 2013

Cash or in-kind transfer 0.699** 0.720** 0.759*** 0.771*** 0.786*** 0.787***

Remittances -0.089 -0.115 0.005 -0.085 -0.063 -0.060

Province/Manicaland

Matabeleland North 1.393*** 1.488*** 1.492*** 1.497*** 1.487*** 1.508***

Matabeleland South 0.175 0.211 0.236 0.235 0.254 0.277

Masvingo 0.318* 0.260 0.332* 0.331* 0.342* 0.350*

Constant -5.475** -6.641*** -5.157** -5.132** -5.308** -5.657**

Observations 1537 1537 1537 1537 1537 1537

Log likelihood -839.196 -821.576 -816.544 -816.737 -815.215 -810.442





Table 29: Relationships between household resilience capacity elements and adequate food consumption 2014

p(adequate food consumption) (logit) 2014

Shocks

Maize meal price -1.297** -1.427*** -1.440*** -1.501*** -1.440*** -1.453*** -1.492*** -1.477***

Goat prices -0.006 -0.005 -0.009 -0.008 -0.009 -0.009 -0.009 -0.009

Drought -0.002 -0.002 -0.002* -0.002* -0.002* -0.002 -0.002 -0.002

HH resilience capacity elements

Cereal stores 0.003*** Livestock assets 0.053*** Education HH head 0.213*** Count of livelihoods 0.453** Remittances 0.459*** Savings 0.003** HH sold crops to markets, GMB, traders, contractors 0.919**

HH resilience capacity 0.033***

DFSA wards -0.216 -0.182 -0.224 -0.236* -0.224 -0.218 -0.201 -0.219

CSI -0.031*** -0.031*** -0.029*** -0.030*** -0.029*** -0.029*** -0.029*** -0.028***

Negative coping -0.032 -0.002 -0.020 -0.042 -0.020 -0.020 -0.018 -0.032

Household characteristics Household size -0.017 -0.023 -0.029 -0.031 -0.029 -0.033 -0.031 -0.037

Female headed household -0.385*** -0.389*** -0.396*** -0.392*** -0.396*** -0.396*** -0.394*** -0.373***

Child headed household -0.467 -0.563 -0.412 -0.411 -0.412 -0.503 -0.399 -0.478

Male headed household -0.675** -0.743*** -0.656** -0.638** -0.656** -0.678** -0.672** -0.676**

Education HH head 0.221*** 0.200*** 0.209*** 0.213*** 0.197*** 0.206*** 0.067

Age HH head 0.013*** 0.011*** 0.011*** 0.012*** 0.011*** 0.011*** 0.011*** 0.011***


Climate 0.730*** 0.714*** 0.717*** 0.236 0.717*** 0.710*** 0.711*** 0.540***

Econ-Salary 0.558* 0.524* 0.515 -0.019 0.515 0.423 0.523 0.230

Econ-Wages & trade 0.251 0.273* 0.284* -0.246 0.284* 0.279* 0.295* 0.110

Climate-econ 0.860*** 0.876*** 0.852*** -0.166 0.852*** 0.850*** 0.829*** 0.496**

Mining -0.060 0.039 0.030 -0.771 0.030 -0.002 0.045 -0.254


Middle tercile 0.400** 0.396** 0.400** 0.407** 0.403** 0.368**




p(adequate food consumption) (logit) 2014

Highest tercile 0.881*** 0.873*** 0.881*** 0.858*** 0.868*** 0.624***


Water/sanitation 0.094 0.030 0.045 0.042 0.045 0.027 0.049 0.034

Cash transfer -0.077 -0.095 -0.069 -0.062 -0.069 -0.051 -0.058 -0.030

Loan 0.373* 0.373* 0.374* 0.348 0.374* 0.371* 0.350 0.347

Remittances 0.436*** 0.496*** 0.459*** -0.007 0.461*** 0.462*** 0.286*

Province/Manicaland

Matabeleland North 0.471* 0.306 0.413* 0.441* 0.413* 0.387 0.459* 0.418*

Matabeleland South 1.195*** 0.966*** 1.069*** 1.091*** 1.069*** 1.051*** 1.109*** 1.088***

Masvingo 0.616*** 0.541*** 0.572*** 0.586*** 0.572*** 0.580*** 0.618*** 0.612***

Constant 0.193 0.612 0.283 0.298 0.283 0.316 0.273 0.395

Observations 1671 1679 1671 1671 1671 1671 1671 1671

Log likelihood -941.939 -948.880 -931.932 -928.909 -931.932 -928.682 -929.471 -924.415


Sources: ZimVAC. 2014. Household and community surveys.



Table 30: Relationships between household resilience capacity elements and HDDS 2014

HDDS (OLS) 2014

Shocks Maize meal price -0.812* -0.935** -0.931** -0.977** -0.931** -0.938** -0.987** -1.001**

Goat prices 0.009 0.008 0.005 0.006 0.005 0.005 0.005 0.005

Drought -0.004*** -0.005*** -0.005*** -0.005*** -0.005*** -0.004*** -0.005*** -0.004***


Cereal stores 0.003*** Livestock assets 0.065*** Education HH head 0.184*** Count of livelihoods 0.475*** Remittances 0.573*** Savings 0.003*** HH sold crops to markets, GMB, traders,

contractors 0.669*** HH resilience capacity 0.035***

DFSA wards -0.276*** -0.249** -0.284*** -0.290*** -0.284*** -0.270*** -0.265*** -0.267***

CSI -0.017*** -0.017*** -0.015*** -0.016*** -0.015*** -0.015*** -0.015*** -0.014***

Negative coping -0.070 -0.051 -0.063 -0.086 -0.063 -0.059 -0.061 -0.068

HH characteristics Household size -0.042** -0.047*** -0.052*** -0.053*** -0.052*** -0.056*** -0.053*** -0.059***

Female head HH -0.184* -0.193* -0.194* -0.185* -0.194* -0.189* -0.192* -0.155

Child HH 0.129 0.018 0.178 0.159 0.178 0.081 0.193 0.116

Male head HH -0.231 -0.298 -0.194 -0.165 -0.194 -0.199 -0.203 -0.188

Education HH head 0.197*** 0.175*** 0.178*** 0.184*** 0.161*** 0.177*** 0.019

Age HH head 0.006** 0.004 0.004 0.004 0.004 0.004 0.004 0.003


Climate 0.400*** 0.382*** 0.392*** -0.127 0.392*** 0.368*** 0.385*** 0.169

Econ-Salary 0.713*** 0.648*** 0.686*** 0.118 0.686*** 0.579*** 0.697*** 0.347

Econ-Wages & trade 0.126 0.159 0.168 -0.391** 0.168 0.165 0.175 -0.027

Climate-econ 0.584*** 0.590*** 0.569*** -0.513 0.569*** 0.567*** 0.550*** 0.181

Mining -0.406 -0.289 -0.314 -1.145* -0.314 -0.335 -0.301 -0.602





HDDS (OLS) 2014

Middle tercile 0.401*** 0.397*** 0.401*** 0.411*** 0.404*** 0.370***

Highest tercile 1.010*** 1.001*** 1.010*** 0.980*** 0.995*** 0.709***


Water/sanitation 0.105* 0.032 0.046 0.037 0.046 0.021 0.050 0.030

Cash transfer 0.206 0.161 0.214 0.211 0.214 0.233 0.214 0.244

Loan -0.063 -0.035 -0.064 -0.086 -0.064 -0.081 -0.080 -0.102

Remittances 0.542*** 0.592*** 0.573*** 0.088 0.574*** 0.574*** 0.378***

Province/Manicaland

Matabeleland North 0.375** 0.192 0.347* 0.364** 0.347* 0.306* 0.389** 0.340*


Masvingo 0.660*** 0.592*** 0.625*** 0.630*** 0.625*** 0.633*** 0.669*** 0.668***

Constant 5.466*** 5.947*** 5.536*** 5.542*** 5.536*** 5.569*** 5.534*** 5.689***

Observations 1668 1676 1668 1668 1668 1668 1668 1668

r2 0.201 0.195 0.221 0.227 0.221 0.233 0.224 0.246

Log likelihood -3219.84 -3244.61 -3199.26 -3191.97 -3199.26 -3186.06 -3195.66 -3171.96





Table 31: Relationships between household resilience capacity elements and per capita daily expenditures 2014


Shocks

Maize meal price -1.205 -1.489 -1.553 -1.538 -1.553 -1.621** -1.678 -2.155**

Goat prices -0.003 -0.007 -0.005 -0.003 -0.005 -0.009 -0.005 -0.009

Drought -0.003*** -0.002** -0.003*** -0.003*** -0.003*** -0.003*** -0.003*** -0.003***


Cereal stores 0.000

Livestock assets 0.029***

Education HH head 0.199***

Count of livelihoods 0.233**

Remittances -0.021

Savings 0.002***

HH sold crops to markets, GMB, traders, contractors 0.292


DFSA wards -0.054 -0.104 -0.101 -0.086 -0.101 -0.062 -0.093 -0.143

CSI -0.009*** -0.007** -0.008** -0.008** -0.008** -0.006** -0.008** -0.004

Negative coping -0.012 -0.000 0.024 0.037 0.024 0.034 0.020 0.009


Household size -0.175*** -0.192*** -0.178*** -0.174*** -0.178*** -0.170*** -0.178*** -0.165***

Female head HH -0.105 -0.115 -0.068 -0.040 -0.068 0.035 -0.048 0.067

Child HH -0.374 -0.184 0.086 0.166 0.086 -0.294 0.117 0.086

Male head HH -0.206 -0.245 -0.168 -0.107 -0.168 -0.049 -0.165 -0.082

Education HH head 0.202*** 0.200*** 0.204*** 0.199*** 0.217*** 0.192*** 0.147***

Age HH head -0.000 -0.001 -0.002 -0.001 -0.002 -0.001 -0.002 -0.002


Climate 0.343** 0.285** 0.293** 0.015 0.293** 0.224 0.277* 0.139

Econ-Salary 0.723*** 0.701*** 0.678*** 0.407 0.678*** 0.578*** 0.697*** 0.499**

Econ-Wages & trade 0.156* 0.144 0.140 -0.142 0.140 0.227** 0.116 0.088

Climate-econ 0.376*** 0.357** 0.316** -0.239 0.316** 0.409*** 0.259 0.171

Mining -0.240 -0.093 -0.266 -0.697* -0.266 -0.136 -0.252 -0.351


Middle tercile 0.222 0.269 0.222 0.148 0.242 0.198





Highest tercile 0.570*** 0.595*** 0.570*** 0.410*** 0.576*** 0.326*


Water/sanitation 0.173*** 0.180** 0.167** 0.142* 0.167** 0.090 0.175*** 0.138**

Cash transfer 0.276 0.222 0.317 0.280 0.317 0.267 0.287 0.138

Loan 0.087 0.097 0.077 0.051 0.077 -0.133 0.051 -0.254

Remittances -0.008 -0.005 -0.021 -0.261* 0.042 -0.007 -0.034

Province/Manicaland

Matabeleland North -0.071 -0.112 -0.097 -0.109 -0.097 -0.081 -0.065 0.073

Matabeleland South 0.383*** 0.243** 0.293*** 0.265*** 0.293*** 0.402*** 0.324*** 0.425***

Masvingo 0.099 0.088 0.045 0.039 0.045 0.056 0.095 0.103

Constant 0.388 0.818 0.533 0.392 0.533 0.552 0.596 0.940

Observations 1671 1679 1671 1671 1671 1671 1671 1671

Log likelihood -1532.435 -1512.753 -1497.437 -1488.023 -1497.437 -1413.152 -1493.521 -1435.343





Table 32: Relationships between household resilience capacity elements and moderate to severe hunger 2014

p(Moderate to severe hunger) (logit) 2014

Shocks

Maize meal price 2.173*** 2.369*** 2.505*** 1.963*** 2.021*** 2.042*** 2.025*** 2.040***

Goat prices -0.040*** -0.037** -0.037** -0.033** -0.034** -0.033** -0.034** -0.034**

Drought 0.003 0.003* 0.004* 0.003 0.003 0.003 0.003 0.003


Cereal stores -0.006***

Livestock assets

-0.141***

Education HH head

-0.246***


0.362

Remittances

-0.316

Savings

-0.005*

HH sold crops to markets, GMB, traders, contractors

-0.111


-0.029*

DFSA wards 0.060 -0.032 0.049 0.047 0.067 0.047 0.064 0.059

Negative coping 1.080*** 1.007*** 1.073***


Household size 0.001 0.008 0.018 0.040 0.042 0.046 0.042 0.047

Female head HH -0.268 -0.317 -0.286 -0.252 -0.262 -0.258 -0.262 -0.276

Child HH 0.394 0.492 0.270 0.385 0.416 0.501 0.413 0.454

Male head HH -0.134 -0.073 -0.249 -0.202 -0.219 -0.178 -0.219 -0.210

Education HH head -0.274*** -0.245***

-0.282*** -0.279*** -0.258*** -0.279*** -0.151

Age HH head 0.003 0.006 0.006 0.005 0.005 0.005 0.005 0.005


Climate -0.433 -0.423 -0.396 -0.673* -0.273 -0.267 -0.272 -0.111

Econ-Salary -1.005 -0.916 -1.018 -1.465** -0.985 -0.776 -0.985 -0.684

Econ-Wages & trade -0.229 -0.306 -0.258 -0.611 -0.175 -0.177 -0.177 -0.022

Climate-econ -0.462 -0.492 -0.452 -1.186* -0.362 -0.368 -0.357 -0.044

Mining -0.300 -0.355 -0.282 -1.077 -0.383 -0.363 -0.385 -0.153


Middle tercile

-0.889*** -0.760*** -0.759*** -0.749*** -0.759*** -0.721***

Highest tercile

-1.396*** -1.356*** -1.354*** -1.300*** -1.353*** -1.121***




p(Moderate to severe hunger) (logit) 2014


Water/sanitation -0.258** -0.137 -0.174 -0.241** -0.246** -0.222* -0.247** -0.239**

Cash transfer -1.570** -1.560** -1.520* -1.469** -1.488** -1.503** -1.488** -1.511**

Loan 0.861*** 0.884*** 0.923*** 0.994*** 1.019*** 1.014*** 1.022*** 1.037***

Remittances -0.291 -0.365 -0.361 -0.692**

-0.306 -0.316 -0.154

Province/Manicaland

Matabeleland North -0.382 -0.098 -0.275 -0.158 -0.186 -0.146 -0.191 -0.190

Matabeleland South -0.127 0.301 0.094 0.212 0.191 0.219 0.187 0.170

Masvingo -0.315 -0.135 -0.240 -0.089 -0.096 -0.101 -0.102 -0.125

Constant -1.309* -2.054*** -1.578** -1.203 -1.219 -1.255 -1.215 -1.312*

Observations 1671 1679 1671 1671 1671 1671 1671 1671

Log likelihood -514.453 -514.432 -501.962 -527.480 -528.544 -526.230 -528.525 -526.405







Shocks

Drought -0.015*** -0.017*** -0.015*** -0.016*** -0.014*** -0.013** -0.016*** -0.017*** -0.015***

Goat prices 0.007 0.009 0.010 0.010 0.007 0.007 0.010 0.010 0.007

Maize meal price 0.016 -0.027 0.028 0.025 0.014 -0.045 0.029 0.037 0.034



Livestock assets 0.066***

Adults w/gt primary educ 0.028

Count of livelihoods 0.032

Information 0.091***

Savings 0.004***


HH sold livestock products to traders, CSC, markets, or contractors (%) 0.660**


DFSA wards 0.073 0.069 0.068 0.063 0.054 0.042 0.065 0.070 0.069

CSI -0.014*** -0.014*** -0.012*** -0.012*** -0.012*** -0.012*** -0.012*** -0.012*** -0.011***

Negative coping -0.189* -0.207** -0.169 -0.171 -0.166 -0.167 -0.167 -0.182* -0.186*


Household size -0.033 -0.041 -0.062** -0.059** -0.066** -0.063** -0.061** -0.065** -0.080***

Female head

HH -0.357*** -0.316** -0.246* -0.258* -0.251* -0.255* -0.256* -0.262* -0.202

Male head HH 0.079 0.002 0.037 0.032 0.043 0.050 0.029 0.038 0.092

Educ HH head 0.148*** 0.150*** 0.129** 0.141*** 0.126** 0.139*** 0.140*** 0.145*** 0.081

Age HH head 0.004 0.002 -0.000 0.000 0.001 0.000 0.000 0.000 0.001

Livelihood risk categories/No regular livelihoods

Climate 0.467*** 0.436*** 0.391** 0.355 0.354** 0.388** 0.389** 0.356** 0.253

Econ-Salary 0.531* 0.472* 0.519* 0.478 0.491* 0.427 0.526* 0.534* 0.385

Econ-

Wages/trade 0.278* 0.249* 0.292** 0.251 0.261* 0.273* 0.292** 0.289* 0.196

Climate-econ 0.456*** 0.491*** 0.470*** 0.401 0.446*** 0.440*** 0.467*** 0.469*** 0.266

Mining 1.248* 1.262* 1.337* 1.286* 1.318* 1.375* 1.338* 1.385* 1.311*





Asset terciles/ Lowest tercile

Middle tercile 0.709*** 0.706*** 0.695*** 0.703*** 0.707*** 0.711*** 0.624***

Highest tercile 1.388*** 1.385*** 1.356*** 1.306*** 1.388*** 1.349*** 1.000***


FSN 0.205* 0.235** 0.200* 0.205* 0.162 0.226* 0.203* 0.184 0.149

Ag/livestock

support 0.308*** 0.302*** 0.211* 0.211* 0.081 0.202* 0.202* 0.204* 0.101

Cash transfer 0.063 0.050 0.126 0.120 0.182 0.134 0.124 0.167 0.232

Loan 0.334 0.333 0.336 0.335 0.290 0.321 0.339 0.343 0.308

Remittances 0.667*** 0.657*** 0.660*** 0.658*** 0.655*** 0.631*** 0.657*** 0.661*** 0.565***

Province/Manicaland

Matabeleland N. 0.362 0.166 0.195 0.195 0.329 0.195 0.217 0.249 0.346

Matabeleland S. 0.770*** 0.603** 0.505* 0.507* 0.651** 0.443 0.531* 0.561** 0.654**

Masvingo 0.349** 0.388** 0.341* 0.339* 0.394** 0.348** 0.357** 0.417** 0.468***

Constant -1.792*** -1.795*** -1.886*** -1.900*** -1.852*** -1.654*** -1.930*** -2.025*** -1.910***

Observations 1795 1795 1795 1795 1795 1794 1795 1795 1794

Log likelihood -1085.555 -1094.382 -1061.691 -1061.757 -1054.611 -1053.179 -1061.371 -1058.322 -1049.699


Sources: ZimVAC. 2015 Household and community surveys. AFDM 2017.




HDDS (OLS) 2015

Shocks Drought -0.011*** -0.013*** -0.010*** -0.011*** -0.010** -0.010*** -0.011*** -0.011*** -0.010***

Goat prices 0.021*** 0.021*** 0.022*** 0.022*** 0.020*** 0.022*** 0.022*** 0.022*** 0.021***

Maize meal price 0.058 0.006 0.061 0.039 0.030 0.031 0.047 0.043 0.047


Cereal stores 0.003*** Livestock assets 0.027*** Adults w/gt primary education 0.129***


Information 0.050*** Savings 0.000 HH sold crops to markets, GMB, traders, contractors 0.296 HH sold livestock products to traders, CSC, markets, or contractors (%) 0.132


DFSA wards -0.001 -0.025 -0.016 -0.035 -0.038 -0.037 -0.034 -0.034 -0.031

CSI -0.013*** -0.014*** -0.012*** -0.012*** -0.012*** -0.012*** -0.012*** -0.012*** -0.012***

Negative coping -0.278*** -0.292*** -0.272*** -0.264*** -0.267*** -0.267*** -0.266*** -0.270*** -0.273***


Household size -0.059*** -0.061*** -0.089*** -0.075*** -0.078*** -0.075*** -0.076*** -0.076*** -0.084***

Female head HH -0.235** -0.238** -0.113 -0.165* -0.162* -0.165* -0.163* -0.165* -0.138

Male head HH -0.340* -0.394** -0.355* -0.387** -0.374** -0.382** -0.387** -0.383** -0.353*

Education HH head 0.119*** 0.116*** 0.051 0.109*** 0.096*** 0.107*** 0.106*** 0.108*** 0.077**

Age HH head 0.000 -0.000 -0.003 -0.003 -0.002 -0.003 -0.002 -0.002 -0.002


Climate 0.096 0.101 0.037 0.083 0.011 0.032 0.032 0.027 -0.036

Econ-Salary 0.674*** 0.606*** 0.624*** 0.682*** 0.609*** 0.608*** 0.633*** 0.627*** 0.522**

Econ-Wages & trade 0.144 0.112 0.152 0.191 0.123 0.139 0.144 0.140 0.100

Climate-econ 0.217* 0.223* 0.184 0.280 0.170 0.183 0.181 0.184 0.095

Mining 0.112 0.046 0.106 0.146 0.070 0.091 0.100 0.100 0.065


Middle tercile 0.294*** 0.294*** 0.278*** 0.291*** 0.290*** 0.291*** 0.255***




HDDS (OLS) 2015

Highest tercile 1.004*** 1.014*** 0.982*** 0.999*** 1.007*** 1.000*** 0.820***


FSN 0.271*** 0.282*** 0.251*** 0.268*** 0.246*** 0.270*** 0.267*** 0.265*** 0.238***

Ag/livestock support 0.565*** 0.585*** 0.490*** 0.499*** 0.421*** 0.498*** 0.487*** 0.495*** 0.445***

Cash transfer -0.161 -0.184 -0.087 -0.110 -0.076 -0.109 -0.106 -0.102 -0.056

Loan 0.538*** 0.516*** 0.535*** 0.538*** 0.505*** 0.535*** 0.539*** 0.535*** 0.521***

Remittances 0.214* 0.242** 0.225** 0.223** 0.213* 0.217* 0.220* 0.221* 0.171

Province/Manicaland

Matabeleland N. -0.483** -0.521** -0.558*** -0.569*** -0.497** -0.572*** -0.542*** -0.556*** -0.507**

Matabeleland S. -0.006 -0.037 -0.191 -0.188 -0.113 -0.191 -0.161 -0.176 -0.121

Masvingo 0.070 0.129 0.070 0.063 0.087 0.066 0.081 0.079 0.125

Constant 4.360*** 4.374*** 4.482*** 4.417*** 4.461*** 4.446*** 4.390*** 4.390*** 4.445***

Observations 1783 1783 1783 1783 1783 1782 1783 1783 1782

r2 0.210 0.194 0.231 0.227 0.231 0.227 0.228 0.227 0.233

Log likelihood -3375.116 -3393.024 -3351.978 -3355.995 -3351.469 -3354.270 -3355.102 -3355.780 -3347.451


Sources: ZimVAC. 2015 Household and community surveys. AFDM 2017





Shocks

Drought 0.006 0.003 0.006 0.005 0.009* 0.008 0.005 0.003 0.005

Goat prices 0.021*** 0.022*** 0.023*** 0.023*** 0.018*** 0.023*** 0.023*** 0.021*** 0.020***

Maize meal price 0.271 0.225 0.358*** 0.333*** 0.279** 0.326*** 0.333*** 0.312** 0.339***



Livestock assets 0.005*

Adults w/gt primary educ 0.091*


Information 0.070***

Savings 0.001***


HH sold livestock products to traders, CSC, markets, or contractors (%) 0.442***


DFSA wards 0.036 0.014 0.043 0.036 0.039 0.046 0.038 0.024 0.033

CSI -0.008*** -0.009*** -0.006*** -0.006*** -0.006*** -0.005*** -0.006*** -0.006*** -0.005***

Negative coping -0.131 -0.122 -0.030 -0.032 -0.072 -0.019 -0.032 -0.032 -0.048


Household size -0.205*** -0.209*** -0.248*** -0.236*** -0.239*** -0.254*** -0.235*** -0.226*** -0.246***

Female head HH -0.359*** -0.360** -0.321** -0.365*** -0.373*** -0.378*** -0.357*** -0.323*** -0.320***

Male head HH -0.014 -0.030 0.031 0.003 0.032 -0.032 0.015 -0.014 0.015

Educ HH head 0.109*** 0.106*** 0.065** 0.094*** 0.054* 0.093*** 0.092*** 0.087*** 0.044

Age HH head -0.000 -0.001 -0.002 -0.003 -0.003 -0.003 -0.002 -0.002 -0.004


Climate 0.334*** 0.349*** 0.247** 0.275** 0.219* 0.259** 0.241** 0.145 0.192

Econ-Salary 0.630*** 0.639*** 0.601*** 0.637*** 0.627*** 0.536*** 0.617*** 0.624*** 0.486***

Econ-Wages &

trade 0.076 0.062 0.071 0.092 0.053 0.067 0.072 0.042 0.033

Climate-econ 0.264* 0.272* 0.239* 0.288 0.234** 0.256** 0.241* 0.205* 0.170

Mining -0.351 -0.416 -0.445 -0.413 -0.382* -0.414 -0.430 -0.434 -0.475*






Middle tercile 0.395*** 0.390*** 0.386*** 0.397*** 0.387*** 0.389*** 0.364***

Highest tercile 0.678*** 0.677*** 0.628*** 0.671*** 0.678*** 0.606*** 0.518***


FSN 0.076 0.067 0.066 0.066 0.053 0.048 0.064 0.062 -0.003

Ag/livestock

support 0.280*** 0.315*** 0.207*** 0.209*** 0.064 0.188** 0.214*** 0.169** 0.195**

Cash transfer -0.103 -0.126 -0.036 -0.055 0.040 -0.007 -0.054 -0.014 0.028

Loan 0.506*** 0.462*** 0.558*** 0.567*** 0.466*** 0.576*** 0.564*** 0.534*** 0.498***

Remittances 0.268*** 0.282*** 0.267*** 0.269*** 0.301*** 0.276*** 0.263*** 0.246** 0.212**

Province/Manicaland

Matabeleland N. -1.055*** -1.001*** -1.129*** -1.139*** -0.995*** -1.169*** -1.132*** -1.058*** -1.034***

Matabeleland S. -0.284 -0.226 -0.426** -0.414* -0.327* -0.463** -0.409* -0.330* -0.285

Masvingo -0.356** -0.264 -0.327** -0.320** -0.322*** -0.296** -0.315** -0.245** -0.217

Constant -0.556 -0.715 -0.663 -0.717 -0.308 -0.451 -0.737 -0.789** -0.521

Observations 1795 1795 1795 1795 1795 1794 1795 1795 1794

Log likelihood -1106.064 -1110.581 -1036.854 -1044.215 -986.601 -991.541 -1043.699 -1010.573 -988.191


Sources: ZimVAC. 2015. Household and community surveys. AFDM 2017



Table 36: Relationships between househld resilience capacity elements and moderate to severe hunger 2015

p(moderate to severe hunger)

Shocks Drought 0.009 0.012* 0.010 0.010 0.010 0.009 0.010 0.012* 0.010

Goat prices 0.021* 0.017 0.018 0.019 0.018 0.019* 0.018 0.019* 0.021*

Maize meal price -0.328 -0.297 -0.352 -0.343 -0.345 -0.334 -0.346 -0.354 -0.356


Cereal stores -0.006*** Livestock assets -0.152*** Adults w/gt primary educ -0.125

Count of livelihoods 0.093 Information 0.004 Savings -0.001 HH sold crops to markets, GMB, traders, contractors -0.053

HH sold livestock products to traders, CSC, markets, or contractors (%) -0.676** HH resilience capacity -0.048**

DFSA wards -0.158 -0.158 -0.175 -0.154 -0.155 -0.147 -0.154 -0.164 -0.155

CSI Negative coping 0.651*** 0.674*** 0.633*** 0.624*** 0.631*** 0.629*** 0.631*** 0.640*** 0.639***


Household size 0.083** 0.100*** 0.118*** 0.107*** 0.106*** 0.106*** 0.106*** 0.112*** 0.117***

Female head HH 0.026 -0.067 -0.152 -0.105 -0.105 -0.108 -0.105 -0.097 -0.136

Male head HH 0.489 0.587* 0.509 0.548* 0.541* 0.529* 0.541* 0.536* 0.505

Educ HH head -0.065 -0.064 -0.003 -0.061 -0.060 -0.060 -0.059 -0.061 -0.020

Age HH head 0.001 0.004 0.005 0.005 0.005 0.004 0.005 0.004 0.004


Climate 0.087 0.144 0.170 0.064 0.166 0.171 0.167 0.211 0.258

Econ-Salary -0.738 -0.642 -0.667 -0.789 -0.670 -0.635 -0.669 -0.675 -0.573

Econ-Wages &

trade 0.249 0.249 0.214 0.119 0.228 0.237 0.229 0.229 0.295

Climate-econ 0.403* 0.341 0.396* 0.185 0.388* 0.408* 0.390* 0.396* 0.533**

Mining -0.202 -0.305 -0.383 -0.449 -0.334 -0.347 -0.335 -0.397 -0.327




Table 36: Relationships between househld resilience capacity elements and moderate to severe hunger 2015

p(moderate to severe hunger)

Middle tercile -0.670*** -0.671*** -0.668*** -0.664*** -0.666*** -0.667*** -0.607***

Highest tercile -1.551*** -1.570*** -1.560*** -1.518*** -1.558*** -1.511*** -1.290***


FSN -0.315** -0.339** -0.268* -0.288* -0.293* -0.297* -0.290* -0.267* -0.249

Ag/livestock

support 0.168 0.257* 0.292** 0.284* 0.283* 0.293** 0.290** 0.299** 0.363**

Cash transfer 0.135 0.053 -0.037 -0.001 0.003 -0.005 -0.000 -0.045 -0.070

Loan -0.012 -0.048 -0.014 -0.021 -0.023 -0.012 -0.021 -0.040 -0.006

Remittances 0.171 0.182 0.199 0.195 0.198 0.212 0.199 0.198 0.269

Province/Manicaland

Matabeleland N. -0.707** -0.411 -0.536 -0.532 -0.527 -0.522 -0.537 -0.590* -0.613*

Matabeleland S. -1.097*** -0.798** -0.853** -0.855** -0.849** -0.832** -0.860** -0.916*** -0.946***

Masvingo -1.397*** -1.410*** -1.398*** -1.394*** -1.388*** -1.392*** -1.393*** -1.467*** -1.463***

Constant -1.269* -1.222* -1.284* -1.224* -1.210* -1.280* -1.207* -1.115 -1.229*

Observations 1795 1795 1795 1795 1795 1794 1795 1795 1794

Log likelihood -710.576 -697.798 -690.958 -691.999 -692.135 -691.456 -692.131 -689.971 -689.211


Sources: ZimVAC. 2015 Household and community surveys. AFDM 2017.





Shocks Drought -0.011* -0.014** -0.010 -0.0102 -0.00765 -0.00701 -0.00984 -0.008

Crop/livestock

shocks -0.074 -0.123** -0.136*** -0.136*** -0.135** -0.127** -0.134** -0.137***

DFSA wards -0.265*** -0.243** -0.258** -0.259** -0.260** -0.241** -0.259** -0.239**

CSI -0.019*** -0.017*** -0.0166*** -0.016*** -0.0160*** -0.0164*** -0.0165*** -0.017***

Negative coping -0.207** -0.214** -0.221** -0.220** -0.198** -0.207** -0.217** -0.204**


Cereal stores 0.001*** Livestock assets 0.089*** Count of

livelihoods 0.117 Adults w/gt primary educ 0.159*** Savings 0.00393*** HH sold crops to markets, GMB, traders, contractors 1.042*** HH sold livestock products to traders, CSC, markets, or contractors (%) 0.0210



Household size -0.017 -0.034* -0.0380* -0.0591*** -0.0361* -0.0389* -0.0382* -0.050**

Female head HH -0.091 -0.038 -0.0339 0.0265 -0.0384 -0.0312 -0.0353 0.004

Male head HH 0.285 0.262 0.266 0.291 0.309 0.250 0.260 0.304

Education HH head 0.229*** 0.222*** 0.243*** 0.170*** 0.236*** 0.247*** 0.245*** 0.197***

Age HH head 0.004 0.002 0.00173 0.000545 0.00177 0.00164 0.002 0.001


Climate 0.277** 0.219 0.0936 0.212 0.223* 0.202 0.216 0.051

Econ-Salary 0.088 0.080 -0.119 0.0151 0.0201 0.0258 0.0307 -0.122

Econ-Wages &

trade 0.236* 0.257** 0.124 0.256** 0.266** 0.257** 0.256** 0.174

Climate-econ 0.319** 0.281** -0.0269 0.244* 0.224 0.246* 0.244* 0.0113

Asset terciles/lowest tercile

Middle tercile 0.504*** 0.507*** 0.489*** 0.501*** 0.506*** 0.461***





Highest tercile 1.108*** 1.108*** 1.064*** 1.094*** 1.110*** 0.831***


Water/sanitation 0.298*** 0.231*** 0.206*** 0.192*** 0.199*** 0.218*** 0.208*** 0.206***

FSN 0.238** 0.283*** 0.266*** 0.264*** 0.264*** 0.264*** 0.262*** 0.270***

Ag/livestock

support 0.302*** 0.271*** 0.259*** 0.258*** 0.268*** 0.249*** 0.260*** 0.239**

Loan 0.044 0.070 0.0640 0.0627 0.0556 0.0538 0.0497 0.0515

Remittances 0.671** 0.605** 0.644** 0.661** 0.570** 0.637** 0.650** 0.433

Province/Manicaland

Matabeleland N. 0.221 -0.027 -0.0498 0.0219 -0.139 -0.0413 -0.0545 -0.081

Matabeleland S. 0.889*** 0.726*** 0.673*** 0.724*** 0.612*** 0.666*** 0.670*** 0.660***

Masvingo 1.347*** 1.308*** 1.221*** 1.204*** 1.185*** 1.224*** 1.220*** 1.233***

Constant -2.019*** -1.899*** -1.912*** -1.777*** -1.851*** -1.864*** -1.927*** -1.777***

Observations 2349 2349 2349 2349 2349 2349 2349 2349

Log likelihood -1454.998 -1430.308 -1415.3 -1411.1 -1407.9 -1412.0 -1415.8 -1405.6


Sources: ZimVAC. 2016. Household surveys AFDM 2017.




HDDS (OLS) 2016

Shocks Drought -0.035*** -0.037*** -0.0325*** -0.0332*** -0.0316*** -0.0299*** -0.0331*** -0.0317***

Crop/livestock shocks -0.130*** -0.150*** -0.181*** -0.178*** -0.175*** -0.167*** -0.177*** -0.179***

DFSA wards -0.214*** -0.196** -0.201** -0.203*** -0.199** -0.182** -0.203*** -0.188**

CSI -0.011*** -0.010*** -0.00920*** -0.00879*** -0.00880*** -0.00883*** -0.00897*** -0.00899***

Negative coping -0.192*** -0.197*** -0.200*** -0.196*** -0.186*** -0.183*** -0.194*** -0.185***


Cereal stores 0.001*** Livestock assets 0.044*** Count of livelihoods 0.264*** Adults w/gt primary educ 0.102*** Savings 0.00152*** HH sold crops to markets, GMB, traders, contractors 1.039*** HH sold livestock products to traders, CSC, markets, or contractors (%) 0.0445



Household size -0.020 -0.028* -0.0350** -0.0488*** -0.0325** -0.0362** -0.0356** -0.0430***

Female head HH -0.110 -0.087 -0.0601 -0.0247 -0.0695 -0.0576 -0.0630 -0.0348

Male head HH 0.085 0.073 0.0636 0.0714 0.0757 0.0410 0.0491 0.0880


Age HH head 0.005** 0.004* 0.00276 0.00197 0.00253 0.00258 0.00262 0.00196


Climate 0.505*** 0.477*** 0.173 0.445*** 0.449*** 0.434*** 0.449*** 0.330***

Econ-Salary 0.517*** 0.497*** 0.118 0.441** 0.444** 0.444*** 0.451*** 0.346**

Econ-Wages & trade 0.162* 0.168* -0.136 0.160* 0.167* 0.164* 0.162* 0.105

Climate-econ 0.674*** 0.663*** -0.00703 0.601*** 0.591*** 0.609*** 0.603*** 0.439***

Mining 0.924 0.952 0.481 0.821 0.821 0.818 0.819 0.733

Asset terciles/lowest tercile 0 0 0 0 0 0

Middle tercile 0.616*** 0.620*** 0.608*** 0.614*** 0.620*** 0.587***

Highest tercile 0.980*** 0.984*** 0.962*** 0.968*** 0.985*** 0.784***





HDDS (OLS) 2016

Water/sanitation 0.286*** 0.248*** 0.204*** 0.199*** 0.206*** 0.218*** 0.208*** 0.206***


FSN 0.084 0.099 0.0962 0.0864 0.0869 0.0868 0.0858 0.0914

Ag/livestock support 0.255*** 0.235*** 0.207*** 0.212*** 0.210*** 0.200*** 0.213*** 0.193***

Loan 0.189 0.217 0.214 0.194 0.182 0.195 0.188 0.189

Remittances 0.632*** 0.625*** 0.584*** 0.601*** 0.543*** 0.576*** 0.595*** 0.437**

Province/Manicaland 0.000 0.000 0 0 0 0 0 0

Matabeleland North 0.358** 0.257 0.129 0.165 0.0807 0.128 0.118 0.101


Masvingo 1.034*** 1.043*** 0.901*** 0.882*** 0.884*** 0.898*** 0.898*** 0.908***

Constant 2.633*** 2.643*** 2.723*** 2.782*** 2.745*** 2.762*** 2.686*** 2.815***

Observations 2353 2353 2353 2353 2353 2353 2353 2353

r2 0.170 0.176 0.209 0.208 0.211 0.211 0.206 0.214

Log likelihood -4565.170 -4556.088 -4508.0 -4509.3 -4505.5 -4505.1 -4512.7 -4501.3


Sources: ZimVAC. 2016. Household surveys. AFDM 2017.





Shocks Drought -0.010* -0.013 -0.00639 -0.0113 -0.00759* -0.00765 -0.0105 -0.00387

Crop/livestock shocks 0.034 -0.009 0.00119 -0.00513 0.0260 0.0232 -0.00989 -0.0179

DFSA wards -0.069 -0.026 0.00341 -0.0264 0.00582 -0.0148 -0.0255 0.0552

CSI -0.008*** -0.006*** -0.00671*** -0.00555*** -0.00532*** -0.00547*** -0.00549*** -0.00539***

Negative coping 0.064 0.070 0.0278 0.0413 0.0615 0.0478 0.0449 0.0696


Cereal stores 0.001*** Livestock assets 0.028*** Count of livelihoods 0.285*** Adults w/gt primary educ 0.120*** Savings 0.000744*** HH sold crops to markets, GMB, traders, contractors 0.917** HH sold livestock products to traders, CSC, markets, or contractors (%) 0.0872



HH size -0.230*** -0.228*** -0.222*** -0.241*** -0.203*** -0.244*** -0.219*** -0.204***

Female head HH -0.108 -0.033 -0.0712 -0.000462 -0.116 -0.0408 -0.0516 -0.0230

Male head HH 0.086 0.104 0.0957 0.101 0.114 0.00500 0.0827 0.186*


Age HH head -0.001 0.001 -0.000535 0.000123 -0.00178 -0.00170 -0.0000606 0.000407


Climate 0.049 0.059 -0.236** 0.0342 -0.107 0.0233 0.0437 -0.0846

Econ-Salary 0.208** 0.258*** -0.211 0.166* 0.168** 0.128 0.194** 0.134*

Econ-Wages & trade 0.012 0.089 -0.271** 0.0619 0.0409 0.0790 0.0651 0.0155

Climate-econ 0.381*** 0.401*** -0.308** 0.347*** 0.306*** 0.410*** 0.343*** 0.186**

Mining 0.017 0.072 -0.425 0.0415 0.0527 0.139 0.0340 -0.0514


Middle tercile 0.218** 0.207** 0.136 0.182** 0.213** 0.131

Highest tercile 0.469*** 0.481*** 0.457*** 0.482*** 0.479*** 0.278***






Water/sanitation 0.097* 0.096* 0.0829 0.0856* 0.0762** 0.102*** 0.0896* 0.102***

FSN -0.209*** -0.222*** -0.158*** -0.198*** -0.204*** -0.215*** -0.189*** -0.166***

Ag/livestock support 0.221*** 0.187* 0.152 0.182** 0.154*** 0.183** 0.171* 0.0900

Loan 0.240*** 0.315*** 0.317*** 0.292*** 0.232** 0.311*** 0.284*** 0.273***

Remittances 0.304* 0.413** 0.301* 0.375** 0.252 0.312* 0.352** 0.255*

Province/Manicaland 0.000 0.000 0 0 0 0 0 0

Matabeleland North 0.340** 0.145 0.202 0.254* 0.225** 0.268 0.186 0.135


Masvingo 0.161* 0.239*** 0.159** 0.171** 0.178** 0.197** 0.168** 0.177**

Constant -0.948*** -1.100*** -0.914*** -1.155*** -0.968*** -1.049*** -1.130*** -0.924***

Observations 2353 2353 2353 2353 2353 2353 2353 2353

Log likelihood -779.725 -830.313 -769.6 -810.1 -674.8 -761.4 -825.4 -576.1


Sources: ZimVAC. 2016. Household surveys. AFDM 2017.




p(Moderate to severe hunger)

Shocks Drought 0.054*** 0.056*** 0.0533*** 0.0542*** 0.0514*** 0.0529*** 0.0536*** 0.0533***

Crop and/or livestock shocks 0.139** 0.197*** 0.214*** 0.216*** 0.219*** 0.212*** 0.213*** 0.222***

DFSA wards -0.113 -0.140 -0.117 -0.127 -0.110 -0.118 -0.116 -0.128

CSI Negative coping 1.089*** 1.082*** 1.107*** 1.106*** 1.066*** 1.095*** 1.098*** 1.098***


Cereal stores -0.002** Livestock assets -0.103*** Count of livelihoods -0.181 Adults w/gt primary educ -0.234***

Savings

-0.0109***

HH sold crops to markets, GMB, traders, contractors -0.337 HH sold livestock products to traders, CSC, markets, or contractors (%)

-0.0260

HH resilience capacity -0.0539***


HH size 0.014 0.030 0.0305 0.0562** 0.0317 0.0304 0.0304 0.0446*

Female head HH -0.076 -0.138 -0.162 -0.242* -0.153 -0.160 -0.160 -0.201

Male head HH -0.036 -0.014 0.00515 -0.0310 -0.0381 0.00858 0.00712 -0.0300

Education HH head -0.198*** -0.185*** -0.208*** -0.0956 -0.192*** -0.209*** -0.209*** -0.150***

Age HH head -0.001 0.001 0.00234 0.00389 0.00214 0.00245 0.00242 0.00302


Climate 0.118 0.212 0.397* 0.227 0.191 0.207 0.207 0.395**

Econ-Salary -0.414 -0.360 -0.0913 -0.285 -0.259 -0.308 -0.309 -0.139

Econ-Wages & trade -0.092 -0.088 0.110 -0.0865 -0.111 -0.0929 -0.0932 0.00933

Climate-econ -0.438*** -0.377** 0.0559 -0.351** -0.316* -0.363** -0.363** -0.0932

Mining -0.290 -0.348 0.0481 -0.186 -0.219 -0.183 -0.184 -0.0333


Middle tercile -0.698*** -0.695*** -0.681*** -0.699*** -0.699*** -0.651***

Highest tercile -1.227*** -1.218*** -1.154*** -1.226*** -1.226*** -0.914***





p(Moderate to severe hunger)

Water/sanitation -0.147** -0.064 -0.0418 -0.0149 -0.0304 -0.0455 -0.0444 -0.0313

FSN -0.135 -0.171 -0.146 -0.141 -0.153 -0.140 -0.141 -0.144

Ag/livestock support 0.178* 0.219** 0.236** 0.235** 0.220** 0.231** 0.230** 0.248**

Loan 0.264 0.229 0.256 0.263 0.250 0.272 0.274 0.261

Remittances 0.087 0.203 0.157 0.148 0.310 0.149 0.149 0.383

Province/Manicaland

Matabeleland North -0.030 0.183 0.284 0.202 0.420* 0.291 0.291 0.304

Matabeleland South -0.143 0.046 0.108 0.0564 0.201 0.110 0.109 0.118

Masvingo -0.346** -0.356** -0.246 -0.220 -0.201 -0.251 -0.251 -0.263

Constant 1.423*** 1.195** 1.269** 1.049** 1.199** 1.264** 1.284** 1.140**

Observations 2320 2320 2320 2320 2320 2320 2320 2320

Log likelihood -1176.575 -1154.560 -1138.3 -1131.8 -1124.4 -1139.0 -1139.1 -1131.3




APPENDIX E: COMPARING THE EFFECTS OF EXPLANATORY VARIABLES 89

Appendix E: Comparing the effects of explanatory variables

Figures that follow in this section compare explanatory variables in terms of the magnitude of their

effects on coping strategies and outcomes (elasticities). Figures show the change in dependent

variables43: resulting from a one percent change in each explanatory variable. Figures allow for

comparison among variables and across outcomes. Data come from regression equations (tables

are included in Appendix 2). Statistically significant variables (<0.10 from regression equations) are

displayed using blue lines. Variables that are not statistically significant are shown with gray dashes.

Non-overlapping confidence intervals indicate statistically significant differences between variables.

Figures in this section show a consistent relationship between household resilience capacity and

well-being outcomes. In all four years, a one percent increase in household resilience capacity was

associated with increases in FCS and HDDS of about 0.1 percent, increases in per capita daily

expenditures of 0.2 percent and decreases in the probability that a household will experience

moderate to severe hunger of 0.1 percent. Of the well-being outcomes, hunger is the most

sensitive to shocks. For years where price data are included as shock exposure measures, coping

strategies and most outcomes are very sensitive to price changes. Note that prices may be picking

up other information, such as access to markets, services, and infrastructure.

Figure 13 presents comparisons of the effects of household resilience, NGO and government

support, remittances, and shocks on CSI in 2013. The figure shows that even though household

resilience capacity, improved water and/or sanitation, and remittances all reduce CSI by a similar

degree (less than 0.1 percent), CSI is more sensitive to changes in maize prices. A one percent

increase in maize prices is associated with increases in CSI by 3.2 percent (on average).

43 Percent change in CSI, FCS, HDDS, and per capita daily expenditures. Change in probability for negative coping strategies

and household hunger



Figure 13: Comparing the effects of resilience, NGO/govt. support, and

shocks on CSI, 2013

Resilience

NGO/Govtsupport

Shocks

Hh resilience capacity

Water/sanitation

FSN

Cash transfers

Remittances

Maize prices (WFP)

Drought

Lack of inputs

0 1 2 3 4 5

Sources: ZimVAC surveys 2013; AFDM 2017

Figure 14 presents estimates of the magnitude of the effects of household resilience, NGO and

government support, coping strategies, and shocks on well-being outcomes for 2013. Estimates of

the effect of increases in maize meal prices on outcome are presented in a second figure (Figure

14b) because the scale is much wider. Figure 14a shows that household resilience capacity is

associated with improvements in FCS, HDDS, per capita expenditures, and household hunger. A

one percent increase in household resilience capacity is associated with 0.1 percent increase in FCS

and HDDS, about a 0.2 percent increase in per capita daily expenditures, and a 0.1 percent

decrease in the probability that a household will experience moderate to severe hunger. The figure

also shows that remittances were associated with similar levels of improvement in FCS, HDDS, and

per capita daily expenditures. Drought (reported at the household level) had a larger impact on

hunger than FCS or HDDS but no impact on per capita daily expenditures. The second figure

presents similar information for maize prices and shows that a one percent (1 cent) increase in the

price of maize reduces HDDS by about one percent, and increases the probability of moderate to

severe hunger by around nine percent. However, the estimate for the decrease in household

hunger associated with a one cent increase in the price of maize ranges from two percent to 17

percent44.

44 The large range for the estimate is because data are aggregated at a high level. WFP data are for markets, numbering between

one and three in a district.



Figure 14: Comparing the effects of resilience, NGO/govt. support, coping strategies and shocks on

outcomes, 2013

Resilience

NGO/Govt support

Coping

Shocks

Resilience

NGO/Govt support

Coping

Shocks


Water/sanitation

FSN

Cash transfers

Remittances

CSI

Drought

Lack of inputs


Water/sanitation

FSN

Cash transfers

Remittances

CSI

Drought

Lack of inputs

-.4 -.2 0 .2 .4 .6 -.4 -.2 0 .2 .4 .6

FCS HDDS

Per capita daily expenditures p(moderate to severe hunger)

e1

e1




outcomes, 2013

Sources: ZimVAC household surveys 2013; WFP 2017

Shocks

Shocks

Maize prices (WFP)

Maize prices (WFP)

-2 -1 0 1 2 3 4 5 6 7 8 9 10 1112131415 -2 -1 0 1 2 3 4 5 6 7 8 9 10111213 1415

FCS HDDS


e1

e1



Figure 15 presents estimates for 2014 of the magnitude of the effects of household resilience, NGO and government support, remittances,

and shocks on coping strategies. A one percent increase in household resilience capacity is associated with a drop in the CSI of 0.5

percent. A one percent increase in improved water and/or sanitation is associated with decreases in CSI and negative coping of about 0.4

percent. The relationship between loans CSI and negative coping may reflect loan program targeting.

Figure 15: Comparing the effects of resilience, NGO/govt. support, and shocks on coping strategies 2014


Resilience

NGO/Govtsupport

Shocks


Water/sanitation

Cash transfers

Loan

Remittances

Maize meal price

Goat prices

Drought

Lack of inputs

-3 -2 -1 0 1 -3 -2 -1 0 1

CSI p(Negative coping)

e0a

e0a



presents estimates of the magnitude of the effects of household resilience, NGO and government

support, coping strategies, and shocks on well-being outcomes for 2014. Estimates of the effect of

increases in maize meal prices on outcome are presented in a separate figure because the scale is

much wider. The first figure shows that household resilience capacity is associated with

improvements in FCS, HDDS, per capita expenditures, and household hunger. A one percent

increase in household resilience capacity is associated with a 0.1 percent increase in FCS and

HDDS, about a 0.2 percent increase in per capita daily expenditures, and a 0.1 percent decrease in

the probability that a household will experience moderate to severe hunger. The figure also shows

that remittances is associated with improvement in FCS (0.2 percent) and HDDS (one percent).

Drought (reported at the household level) had a larger impact on hunger than FCS or HDDS but

no impact on per capita daily expenditures. The second figure presents similar information for

maize prices and shows that a one percent (1 cent) increase in the price of maize reduces FCS and

HDDS by about 0.1 percent, decreases per capita daily expenditures by 1.5 percent (one and a half

cents) and increases the probability of moderate to severe hunger by 2.2 percent.




outcomes, 2014

Shocks

Shocks

Maize meal price

Maize meal price

-4 -3 -2 -1 0 1 2 3 4 -4 -3 -2 -1 0 1 2 3 4

FCS HDDS


e1

e1




outcomes, 2014


Resilience

NGO/Govt support

Coping

Shocks

Resilience

NGO/Govt support

Coping

Shocks


Water/sanitationCash transfers

LoanRemittances

CSINegative coping

Goat pricesDrought

Lack of inputs


Water/sanitationCash transfers

LoanRemittances

CSINegative coping

Goat pricesDrought

Lack of inputs

-.5 0 .5 1 1.5 -.5 0 .5 1 1.5

FCS HDDS


e1

e1



Figure 17 presents estimates of the magnitude of the effects of household resilience, NGO and government support, remittances, and

shocks on coping strategies in 2015. The figure shows that a one percent increase in household resilience capacity is associated with a

drop in the CSI of 0.3 percent. The positive relationship between agricultural and livestock support and CSI and negative coping may

reflect loan program targeting. Maize meal prices have a similar effect on CSI and negative coping strategies, both increase by about 0.2

percent for a one cent increase in maize meal prices. A one percent increase in goat prices (one dollar) is associated with a 0.5 percent

drop in CSI.

Figure 17: Comparing the effects of resilience, NGO/govt. support, and shocks on coping strategies 2015

Sources: ZimVAC. 2015. household surveys AFDM, 2017

NGO/Govt support

Shocks


FSN

Cash transfers

Ag/livestock support

Loan

Remittances

Rainfall (mm)

Maize meal price

Goat prices

-2 -1 0 1 -2 -1 0 1


e0a

e0a

% change coping strategies for 1% change in X vars




outcomes, 2015


NGO/Govt support

Coping

Shocks

NGO/Govt support

Coping

Shocks


FSNCash transfers

Ag/livestock supportLoan

Remittances

CSINegative coping

Rainfall (mm)

Goat pricesMaize meal price


FSNCash transfers

Ag/livestock supportLoan

Remittances

CSINegative coping

Rainfall (mm)

Goat pricesMaize meal price

-.5 0 .5 1 -.5 0 .5 1

FCS HDDS


e1

e1

% change in outcome for 1% change in X vars



Figure 19: Comparing the effects of resilience, NGO/govt. support, and shocks on

coping strategies, 2016

Sources: ZimVAC household surveys 2016; AFDM, 2017

NGO/Govt support

Shocks


Water/sanitation

FSN

Cash transfers

Ag/livestock support

Loan

Remittances

Rainfall (mm)

Crop and/or livestock shocks

-4 -2 0 2 -4 -2 0 2


e0a

e0a

% change coping strategies for 1% change in X vars




outcomes, 2016


NGO/Govt support

Coping

Shocks

NGO/Govt support

Coping

Shocks


Water/sanitationFSN

Cash transfersAg/livestock support

LoanRemittances

CSINegative coping



Water/sanitationFSN

Cash transfersAg/livestock support

LoanRemittances

CSINegative coping


-.5 0 .5 -.5 0 .5

FCS HDDS


e1

e1

zim babwe resilience res - fsn network · usaid/tango methods was the computation of a household...

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