11 N - Food and Agriculture Organization1 The resilience pillars form part of the RIMA-II...

AnalysIng Resilience for better targeting and action

FA

O r

esil

ien

ce

an

aly

sis

rep

or

tN

o. 1

1RE

SILI

ENC

E IN

DEX

MEA

SURE

MEN

T A

ND

AN

ALY

SIS

II y

RIM

A I

I

2018

Cover picture: © FAO \ Marco Letta

Food and Agriculture Organization of the United NationsRome, 2018

FAO Resilience Analysis report

ANALYSING RESILIENCE FOR TARGETING AND ACTION

No. 11

Resilience Analysis in

JOrdan

This publication has been produced with the assistance of the European Union. The contents of this publication are the sole responsibility of FAO and can in no way be taken to reflect the views of the European Union

The designations employed and the presentation of material in this information product do not imply the expression of any opinion whatsoever on the part of the Food and Agriculture Organization of the United Nations (FAO) concerning the legal or development status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. The mention of specific companies or products of manufacturers, whether or not these have been patented, does not imply that these have been endorsed or recommended by FAO in preference to others of a similar nature that are not mentioned.

The views expressed in this information product are those of the authors and do not necessarily reflect the views or policies of FAO.

ISBN 978-92-5-130146-3

© FAO, 2018

FAO encourages the use, reproduction and dissemination of material in this information product. Except where otherwise indicated, material may be copied, downloaded and printed for private study, research and teaching purposes, or for use in non-commercial products or services, provided that appropriate acknowledgement of FAO as the source and copyright holder is given and that FAO’s endorsement of users’ views, products or services is not implied in any way.

All requests for translation and adaptation rights, and for resale and other commercial use rights should be made via www.fao.org/contact-us/licence-request or addressed to [email protected].

FAO information products are available on the FAO website (www.fao.org/publications) and can be purchased through [email protected].

iii

CONTENTS

ACKNOWLEDGEMENTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v

ACRONYMS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vi

1 KEY MESSAGES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

2 BACKGROUND . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

3 MAIN FINDINGS; POLICY AND PROGRAMMING IMPLICATIONS . . . . . . . . . . . . . . . . . . . . . 11

4 METHODOLOGY AND COVERAGE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

5 NEXT STEPS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

REFERENCES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

ANNEX I – RESILIENCE MEASUREMENT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

ANNEX II – DESCRIPTIVE STATISTICS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

FIGURES

1 Location of Jordan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

2 Pillars’ share of the RCI – Whole sample . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

3 AC pillar variables’ share of AC – Whole sample . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

4 RCI heterogeneity by governorate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

5 Heterogeneity in Food insecurity between governorates (DoS, 2016) . . . . . . . . . . . . . . . . 14

6 Share of RCI pillars by governorate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

7 Share of RCI pillars by gender of the household head . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

8 Share of RCI pillar by nationality of the household head . . . . . . . . . . . . . . . . . . . . . . . . . . 17

9 Share of RCI pillars by nationality of household head in Amman . . . . . . . . . . . . . . . . . . . . 18

10 Share of RCI pillars by nationality of household head in Mafraq . . . . . . . . . . . . . . . . . . . . 18

A1 RIMA-II Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

A2 Share of ABS variables in Jordan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

A3 Share of AST variables in Jordan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

A4 Share of SSN variables in Jordan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

RESILIENCE ANALYSIS IN JORDAN, 2013

iv

TABLES

A1 Definition of resilience pillars and food security indicators . . . . . . . . . . . . . . . . . . . . . . . . 32

A2 Variables emloyed in the RIMA-II model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

A3 MIMIC regression results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

A4 RCI and shocks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

A5 Food expenditure and shocks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

A6 FCS and shocks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

A7 Food expenditure and pillar variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

A8 FCS and pillar variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

A9 Descriptive statistics - Pillar variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

A10 RCI heterogeneity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

v

ACKNOWLEDGEMENTS

This report has been prepared by the Resilience Analysis and Policies (RAP) team of FAO’s Agricultural Development Economics Division (ESA). Special thanks go to Marco Letta, Stefania Di Giuseppe, Marco d’Errico, Rebecca Pietrelli, Doussou Traore and Genevieve Theodorakis, and to Luca Russo from the Resilience Programme Management Team (SP5), for their contributions of technical information.

Tomaso Lezzi and Giorgia Wizemann worked on the formatting and layout of the publication. Alecia Wood completed the editing.

The team acknowledges Rene Verduijn, Craig Von Hagen, Ahmad Hiyari and the Regional Food Security Analysis Network (RFSAN) (a joint FAO and iMMAP initiative), as well as Nasredin Hag Elamin and the whole FAO Jordan team, for their invaluable support.

Thanks are also extended to the Jordanian Department of Statistics for sharing the Household Expenditure and Income Survey (HEIS) 2013 data upon which this analysis is based.


vi

ACRONYMS

ABS Access to Basic Services

AC Adaptive Capacity

ASI Agricultural Stress Index

AST Assets

CSI Coping Strategy Index

DoS Department of Statistics of Jordan

ESCWA United Nations Economic and Social Commission for Western Asia

FA Factor Analysis

FCS Food Consumption Score

FHH Female-Headed Household

GDP Gross Domestic Product

GIEWS Global Information and Early Warning System

HEIS Household Expenditure and Income Survey

HH Household Head

JRP Jordan Response Plan

JRPSC Jordan Response Platform for the Syria Crisis

MENA Middle East and North Africa

MHH Male-Headed Household

MIMIC Multiple Indicators Multiple Causes

MOPIC Ministry of Planning and International Cooperation of Jordan

NDVI Normalized Difference Vegetation Index

OLS Ordinary Least Squares

PoU Prevalence of Undernourishment

RAP Resilience Analysis and Policies (team)

RCI Resilience Capacity Index

RFSAN Regional Food Security Analysis Network

RIMA Resilience Index Measurement and Analysis

RSM Resilience Structure Matrix

SEM Structural Equation Model

vii

SSN Social Safety Nets

TLU Tropical Livestock Units

UNDP United Nations Development Programme

UNHCR United Nations High Commissioner for Refugees

UNICEF United Nations Children’s Fund

UNRWA United Nations Relief and Work Agency for Palestine Refugees in the Near East

USD United States Dollar

WASH Water, sanitation and hygiene

WFP Word Food Programme

Acronyms

© F

AO \ M

arco

Let

ta©

FAO

\ Mar

co L

etta

1

© F

AO \ M

arco

Let

ta

1 KEY MESSAGES This section summarizes the main results of the analysis and related implications for policy and programming.

KEY MESSAGE 1Promoting access to education, productive assets and water services are the three strongest policy avenues for boosting resilience to food security in Jordan.

Adaptive capacity, assets and access to basic services are strongly linked to household resilience capacity in Jordan. This is reflected in the resilience pillar1 of Adaptive Capacity (AC), in particular, which accounts for almost half of the resulting Resilience Capacity Index (RCI) for the overall sample. In turn, AC is almost completely determined by its variables related to the education level of household members (both the average level of education of household members, and of the household head (HH)). Similarly, resilience to food insecurity is reinforced when households have access to productive assets and water services, which are variables that form key elements of the Assets (AST) and Access to Basic Services (ABS) pillars. Therefore, the key driver of resilience capacity in Jordan is primarily education, followed by productive assets and safe and stable access to water.

The importance of education in building resilience is consistent with previous findings by the Department of Statistics of Jordan (DoS), which found that food security is linked to a large extent to the education level of the HH (DoS, 2016). Non-productive assets and a higher number of working age household members also positively contribute to the resilience of households.

Conversely, the Social Safety Nets (SSN) pillar (which comprises both public and private transfers as variables) plays a very small and negligible role in determining resilience to food insecurity in Jordan at the national level.

1 The resilience pillars form part of the RIMA-II methodology, which is used in this report. The resilience pillars selected for this analysis are: Access to Basic Services (ABS), Assets (AST), Social Safety Nets (SNN), and Adaptive Capacity (AC). Further information on the variables that comprise each resilience pillar, how they are estimated, and the full RIMA-II methodology can be found in Annex 1.

© F

AO \ M

arco

Let

ta


2

These results indicate the need for policies to be designed for the least resilient areas of Jordan to foster higher education levels, and to promote investments aimed at improving water infrastructure and water availability at the household level, since such services play a decisive role in determining households’ ability to withstand shocks and stressors.

The commitment of the Jordanian government to increase access to inclusive education, as outlined in the Jordan Response Plan (JRP) 2017-2019 (MOPIC, 2017), is a step forward in this direction. However, a more disaggregated approach is needed to better target vulnerable categories (such as female-headed households (FHHs) – see Key Message 3) and geographic areas.

Regarding water policies, the main recommendation to improve access to basic social services for households is through ongoing water health programmes, as outlined by the Jordanian government in the JRP 2017-2019 (MOPIC, 2017).

In this respect, UN agencies (namely the Food and Agriculture Organization of the United Nations (FAO), the United Nations Children’s Fund (UNICEF), and the World Food Programme (WFP)) and partners should play a key role in essential activities such as water quality monitoring, and project management and technical support in wastewater reuse. These policies should especially focus on the least resilient governorates2 (see Key Message 2).

Policies aimed at building the productive and non-productive asset base of households in Jordan should also be a key area of collaboration and intervention by FAO and partners together with the Government of Jordan.

KEY MESSAGE 2Closing gaps in access to water services at the regional level could play an important role in diminishing regional inequalities in household resilience capacity.

Regional disparities are the most important source of inequality with reference to household resilience capacity in Jordan. There are in fact large differences in resilience capacity at the governorate level. Irbid is the governorate with the highest RCI of 68.3 out of 100, while Zarqa has the lowest RCI (54.2), followed by Mafraq (54.7). These results are again consistent with previous findings by the DoS with regard to vulnerability to food insecurity (DoS, 2016).

While education is shown to be the main determinant of resilience capacity in all governorates, there is considerable variation between governorates in access to basic services. This is mainly driven by inequality in safe and stable access to water. Finally, transfers play a negligible role in almost all governorates, with partial exceptions where social safety nets seem to matter more (namely in the governorates of Karak and Tafiela).

Addressing regional inequality should be considered a top priority by national and international policymakers. In this respect, policies on water services in the least resilient governorates should be coupled with an increase in public investment in particularly vulnerable geographic areas and pockets of food insecurity in the country.

2 A governorate (known as a muhafazah in Arabic) is the first-level administrative division of Jordan. The country is currently divided into twelve governorates: Amman, Balqa, Zarqa, Madaba, Irbid, Mafraq, Jarash, Ajloun, Karak, Tafiela, Ma’an, Aqaba. All of them are included in the analysis carried out in this report.

3

Chapter 1 – Key messages

KEY MESSAGE 3Although FHHs are significantly less resilient than MHHs, encouraging increased access to basic services and education could reduce disparities in resilience capacity between genders.

FHHs have lower access to basic services and are less educated (and hence they have lower figures for the AC pillar) compared to MHHs. Furthermore, monetary transfers are significantly more relevant to FHHs than to MHHs for influencing resilience capacity. Therefore, there is evidence that while transfers (especially public ones, given these are the main driver of the SSN pillar) make up in part for the gap in resilience capacity between MHHs and FHHs, these only partially offset the significant variation in resilience capacity between MHHs and FHHs.

Consequently, FHH access to higher levels of education and to essential services must be increased. Investment targeting these issues is thus key, while public transfers for FHHs are and will be an important tool for policymakers until the gender gap in resilience capacity is eventually closed.

KEY MESSAGE 4Although Jordanian-headed households excel in resilience capacity relative to non-Jordanian-headed households, improving access to public services and transfers can help enhance the resilience capacity of non-Jordanian-headed households.

Jordanian-headed households are significantly more resilient than non-Jordanian-headed households.3 Non-Jordanian-headed households have fewer assets and lower access to services, such as water. As in the case of the above-mentioned gender gap, these deficiencies are partially compensated for by the more significant role of public transfers and subsidies from the National Aid Fund for non-Jordanian-headed households.

These findings are true for both the least resilient, rural governorates (namely, Mafraq) and the most resilient and urbanized ones (namely, Amman), suggesting this heterogeneity is widespread throughout the country. Even though the dataset employed in this analysis is not representative of the share of non-Jordanian nationalities in the country (see Section 2 Methodology and Coverage), which prevents a more in-depth analysis, these findings point to the need for targeted interventions to reduce inequality within the key drivers of resilience capacity (in particular assets and access to basic services) between nationalities at the national, governorate and district levels.

KEY MESSAGE 5Food shortages, household illnesses and droughts are the most significant shocks impacting resilience to food insecurity in Jordan, prompting the need for policy tools such as early warning mechanisms and initiatives to boost household income.

Food shortages, illness of household members and droughts (especially if prolonged over time) are the types of shocks that most significantly and negatively affect the resilience to food insecurity of households in Jordan.

3 Non-Jordanian-headed households include all households whose household head is not Jordanian and falls into one of the other nationality categories available in the survey, namely Syrian, Egyptian, Iraqi, Other Arab and Non-Arab. See Section 2, ‘Methodology and Coverage’ for more detailed information.


4

© F

AO \ R

icca

rdo

Gan

gale

FAO together with other international actors should prioritize investments to target food stability, which in this instance is the most important of the four dimensions of food security,4 as well as sanitation and medical services.

In this respect, the fact that the JRP 2017-2019 presents specific objectives regarding food security (for instance, Specific Objective 1 of the food security section) and sanitation (for instance, Specific Objective 2 of the water, sanitation and hygiene (WASH) section) present appropriate solutions for the medium term, and should be attributed technical assistance and support by international actors in the implementation phase.

However, more comprehensive and long-term interventions are also needed to foster diversification of income sources at the household level, so as to make households less vulnerable to unpredictable shocks and stressors.

Furthermore, contingency plans and early warning mechanisms for recurring exogenous shocks, such as droughts, and provision of timely information to Jordanian households to assist in disaster preparation and mitigation are also key in reducing risk exposure. This is especially the case in view of the future impacts of climate change in the region.5

4 The other three dimensions of food security are food availability, access and utilization. For food security to be achieved, all four dimensions must be fulfilled simultaneously.

5 The impacts of climate change in Jordan are predicted to be severe, especially because they will exacerbate issues such as water stress and desertification, which already represent serious challenges for the country (World Bank, 2016). See the ‘Background’ section of this report for further detail on water stress in Jordan.

© F

AO \ R

icca

rdo

Gan

gale

© F

AO \ M

arco

Let

ta

7

© F

AO \ M

arco

Let

ta

2 BACKGROUNDThis section introduces background information on Jordan.

The Hashemite Kingdom of Jordan – referred to hereafter as Jordan – is an Arab country in West Asia (see Figure 1), with a population of approximately 9.5 million people (World Bank, 2017a), of which the vast majority (84 percent), live in urban areas (World Bank, 2017b), and a significant share (more than two million people, according to the United Nations Relief and Work Agency for Palestine Refugees in the Near East (UNRWA) (2017)), are registered Palestinian refugees. Jordan is an upper middle-income country, ranking 86th in the Human Development Index (UNDP, 2017). Despite this, the country currently faces significant challenges to its socioeconomic system; economic growth in recent years has been sluggish, and the rate of unemployment reached a historic high of 15.3 percent in 2016 (World Bank, 2017c).

The geopolitical situation is also very delicate. As of August 2017, Jordan is home to more than 660 000 UN-registered Syrian refugees6 (WFP, 2017). While Jordan has traditionally been able to maintain its stability despite being located in one of the most unstable regions in the world, regional tensions – especially considering the operational requirements and socioeconomic impacts of the influx of Syrian refugees into Jordan – are placing strain on the Jordanian economy (UNDP and UNICEF, 2015).

Jordan is located in the Middle East and North Africa (MENA) region, often considered one of the most food insecure regions in the world. Pockets of persistent food insecurity in the country are present, especially in communities and areas that were affected by the 2008 economic crisis and those that are now hosting refugees (WFP, 2017). These factors likely contributed to a rise in the Prevalence of Undernourishment (PoU), which increased by more than 50 percent from 2009 to 20127 (World Bank, 2017d).

6 The influx of Syrian refugees entering Jordan started at the outbreak of the Syrian civil war in 2011. As of 2017, 21 percent of registered refugees in Jordan are living in refugee camps. Almost 90 percent of refugees in Jordan are concentrated in four governorates, namely Amman, Irbid, Mafraq and Zarqa. This refugee crisis has been a source of political tension and increasing pressure on the Jordanian socioeconomic system, as well as a strain on the country’s already scarce water resources.

7 Authors’ calculation from World Bank data (World Bank, 2017d).


8

© F

AO \ R

icca

rdo

Gan

gale

Jordan also faces extreme water scarcity, being among the world’s four most water-stressed countries (WFP, 2017). As reported by the Jordan Response Platform for the Syria Crisis (JRPSC), by the start of the Syria crisis the demand for water resources had increased by as much as 40 percent in areas heavily populated by Syrian refugees (MOPIC, 2017). On top of this, the country is faced with a steep increase in water demand as a result of rapid population growth, income growth and urbanization (World Bank, 2016).

According to World Bank (2017d), value-added agriculture only accounts for less than 4 percent of Gross Domestic Product (GDP) in Jordan. This, coupled with frequent and prolonged droughts and desertification, makes Jordan a net importer of food. Despite this, in some rural areas, especially in governorates that are traditionally the poorest, agriculture still represents the main source of income at the household level.

This analysis has been conducted using the 2013 Household Expenditure and Income Survey (HEIS) dataset provided by the DoS. The 2013 HEIS survey covers the whole country, making the sample representative not only at the national level, but also at the governorate, district and sub-district levels. The RIMA-II8 econometric analysis was carried out using a sample of 7 898 households taken from the 2013 HEIS survey. This subsample is still representative thanks to the use of sampling weights provided by the DoS, which were consistently applied across the entire analysis.

Figure 1. Location of Jordan

8 Annex I includes an explanation of the RIMA-II methodology, and details of how the RCI and resilience pillars are estimated from observed variables.

Source:Own elaboration.

© F

AO \ R

icca

rdo

Gan

gale

Jordan also faces extreme water scarcity, being among the world’s four most water-stressed countries (WFP, 2017). As reported by the Jordan Response Platform for the Syria Crisis (JRPSC), by the start of the Syria crisis the demand for water resources had increased by as much as 40 percent in areas heavily populated by Syrian refugees (MOPIC, 2017). On top of this, the country is faced with a steep increase in water demand as a result of rapid population growth, income growth and urbanization (World Bank, 2016).

According to World Bank (2017d), value-added agriculture only accounts for less than 4 percent of Gross Domestic Product (GDP) in Jordan. This, coupled with frequent and prolonged droughts and desertification, makes Jordan a net importer of food. Despite this, in some rural areas, especially in governorates that are traditionally the poorest, agriculture still represents the main source of income at the household level.

This analysis has been conducted using the 2013 Household Expenditure and Income Survey (HEIS) dataset provided by the DoS. The 2013 HEIS survey covers the whole country, making the sample representative not only at the national level, but also at the governorate, district and sub-district levels. The RIMA-II8 econometric analysis was carried out using a sample of 7 898 households taken from the 2013 HEIS survey. This subsample is still representative thanks to the use of sampling weights provided by the DoS, which were consistently applied across the entire analysis.

Figure 1. Location of Jordan

8 Annex I includes an explanation of the RIMA-II methodology, and details of how the RCI and resilience pillars are estimated from observed variables.

© F

AO \ M

arco

Let

ta©

FAO

\ Kha

lil M

azra

awi

11

© F

AO \ M

arco

Let

ta

3 MAIN FINDINGS; POLICY AND PROGRAMMING IMPLICATIONSThis section presents the main findings of the RIMA-II analysis and provides related policy and programming implications.

MAIN FINDING 1Of the four pillars, AC represents the most important pillar in determining household resilience capacity in this sample; within this, the variables related to education levels are the most important component of the RCI.

The importance of education in determining resilience capacity in Jordan can be further understood by examining the share of household RCI attributed to the AC pillar (see Figure 2) – which is by far the most important of the four pillars – and the share of AC pillar variables that make up the pillar itself (see Figure 3), in which the education level (both the average education level of household members and the education level of the HH) accounts for the majority of that pillar.

This is not a surprising result, since generally more educated people are also usually wealthier on average, which consequently makes them more resilient. Furthermore, Jordan is a highly urbanized country that has reached a relatively high average level of education and has successfully achieved universal primary education for its population; statistically the variables related to education have proven to play a primary role in determining people’s ability to cope with shocks in Jordan.

Indeed, this finding is consistent with previous DoS findings that employed the same dataset: “Food insecurity is linked to a large extent to the education level of the head of household. Households whose heads are illiterate or barely literate are more at risk of food insecurity and vulnerability” (DoS, 2016).

As the most important determinant of resilience capacity, the variables that make up the AC pillar could be further strengthened by investments in factors aside from education, such as the dependency ratio (the number of employed people per household compared to the number of unemployed people). Looking at Figure 3 above, in fact, the data suggests that improvements in the dependency ratio (ie. a higher number of employed people relative to unemployed people) could further bolster resilience.

© F

AO \ K

halil

Maz

raaw

i


12

ABS

AST

SSN

AC

Figure 2. Pillars’ share of the RCI – Whole sample

Average education

HH Education

Dependency ratio

Participation Index

Figure 3. AC pillar variables’ share of AC – Whole sample

The relatively low contribution of the dependency ratio to the AC pillar for Jordan could be a product of the country’s young population – in 2017, half of the Jordanian population was under the age of 22.

Source:Authors’ own calculation,

based on HEIS (2013).



13

Chapter 3 – Main findings; policy and programming implications

ABS

AST

SSN

AC

Figure 2. Pillars’ share of the RCI – Whole sample

Average education

HH Education

Dependency ratio

Participation Index

Figure 3. AC pillar variables’ share of AC – Whole sample

The relatively low contribution of the dependency ratio to the AC pillar for Jordan could be a product of the country’s young population – in 2017, half of the Jordanian population was under the age of 22.

Provided that Jordan’s economy can continue to generate jobs for new entrants to the labour market, the dependency ratio will likely play a growing role in promoting household adaptive capacity, and food security resilience more broadly. Similar logic can also be applied to the participation index, which is based on the number of income-generating activities undertaken by a household. This currently makes a marginal contribution to the AC pillar, and thus consequently to household resilience capacity as well.

h Given the preeminent role played by education in determining households’ resilience to food insecurity in Jordan, the primary recommendation for policymakers that stems from this analysis is to increase access to education for the least resilient populations (see Main Finding 2 below) in order to empower them to cope with shocks and reduce their vulnerability by improving the main driver of resilience capacity. In particular, enhancing access to education will be crucial for improving the performance of other variables such as the participation index.

h The section regarding education in the JRP 2017-2019 (MOPIC, 2017) mainly focuses on the improvement of education authorities and facilities; while this is indeed positive, a more targeted approach is needed in order to enhance education opportunities for the least resilient populations (e.g. young women).

h In order to enhance food security, specific programmes should target an increase in income diversification at the household level, especially for vulnerable categories, which would boost adaptive capacity and consequently household resilience capacity.

POLICY AND PROGRAMMING IMPLICATIONS

MAIN FINDING 2Heterogeneity in household resilience capacity is detected at multiple levels: governorate, gender of the HH, and nationality of the HH. Resilience capacity inequality is thus very high in Jordan.

Households with male and Jordanian HHs that are located in the most affluent governorates are better off in terms of resilience capacity; FHHs and non-Jordanian-headed households located in the poorest governorates are the worst off groups. This sharp and widespread heterogeneity in resilience capacity is very statistically significant and mostly driven by a divergence in education levels of household members, productive and non-productive asset ownership, and access to essential services such as water.


14

2.1 HETEROGENEITY AT THE GOVERNORATE LEVELFigure 4 below shows resilience heterogeneity by governorates. Irbid and Ajloun are the governorates with the highest household resilience capacity, while Zarqa, Balqa and Mafraq are the least resilient ones.

0

10

20

30

40

50

60

70

Res

ilien

ce C

apac

ity In

dex

AmmanBalqa

Zarqa

MadabaIrb

id

Mafraq

Jarash

Ajlun

Karak

TafielaMa’an

Aqaba

Figure 4. RCI heterogeneity by governorate

These findings are consistent with previous analyses by the DoS, which also detected strong regional heterogeneity in vulnerability to food insecurity (see Figure 5).

0

2

4

6

8

10

Ajloun

AmmanAqaba

BalqaIrb

id

Jarash

Karak

Ma’an

Madaba

Mafraq

TafielaZarq

a

Food insecure HHs

Vulnerable HHs

Figure 5. Heterogeneity in Food Insecurity between governorates (DoS, 2016)

This discrepancy in resilience capacity across governorates can be explained by a number of factors. Figure 6 below shows a comparison between the share of resilience pillars for the RCIs of each governorate. While the evidence suggests that education variables within the AC pillar are the most important component of household resilience in all governorates, the roles of the other pillars (AST, ABS and SSN) in determining resilience capacity exhibit a large degree of variation between governorates.

0

20

40

60

80

100

Com

pone

nts

shar

e (%

)

Amman Balqa Zarqa Madaba Irbid Mafraq Jarash Ajlun Karak Tafiela Ma’an Aqaba

ABS

AST

SSN

AC

Figure 6. Share of RCI pillars by governorate

Assets (both productive and non-productive, agricultural and non-agricultural) are an important component of resilience capacity in all governorates, but their contribution to resilience is much higher in some governorates (Ma’an and Ajlun) than in others (Amman and Tafiela). However, despite this strong heterogeneity in terms of the importance of the AST pillar, this heterogeneity does not correspond to the division between the most resilient and least resilient governorates, and there is not a clear pattern that can be discerned to explain the variation of this pillar in determining the RCI.

There is a significant discrepancy in access to basic services across governorates. The figures for the ABS pillar (represented in blue in Figure 6) are very low in some governorates (Ma’an, Irbid, Jarash and Zarqa) and highest in Amman, the most populated and urbanized governorate; the most significant variable within the ABS pillar is safe and stable access to (public) water (see Figure A2 in Annex I). Despite being the most resilient governorate, Irbid likely has limited access to basic services relative to other, more urbanized governorates due to the fact it is located in the north of the country, where water stress and increased demand for water have increased as a consequence of the influx of Syrian refugees, according to the Jordanian government (MOPIC, 2017).

The SSN pillar is the lowest contributor to household food security in all governorates, irrespective of their level of resilience capacity. The only governorates where public transfers are a relevant driver of resilience capacity are Karak and Tafiela (which score below average in terms of resilience capacity – see Table A10); in the most resilient governorates – Irbid, Ajloun and Amman – the role of transfers in determining resilience capacity is very close to zero, due to the fact that households in these governorates are more resilient and thus there is less or no need for safety nets.



Source:Author’s reproduction

from DoS (2016).

15


2.1 HETEROGENEITY AT THE GOVERNORATE LEVELFigure 4 below shows resilience heterogeneity by governorates. Irbid and Ajloun are the governorates with the highest household resilience capacity, while Zarqa, Balqa and Mafraq are the least resilient ones.

0

10

20

30

40

50

60

70

Res

ilien

ce C

apac

ity In

dex

AmmanBalqa

Zarqa

MadabaIrb

id

Mafraq

Jarash

Ajlun

Karak

TafielaMa’an

Aqaba

Figure 4. RCI heterogeneity by governorate

These findings are consistent with previous analyses by the DoS, which also detected strong regional heterogeneity in vulnerability to food insecurity (see Figure 5).

0

2

4

6

8

10

Ajloun

AmmanAqaba

BalqaIrb

id

Jarash

Karak

Ma’an

Madaba

Mafraq

TafielaZarq

a

Food insecure HHs

Vulnerable HHs

Figure 5. Heterogeneity in Food Insecurity between governorates (DoS, 2016)

This discrepancy in resilience capacity across governorates can be explained by a number of factors. Figure 6 below shows a comparison between the share of resilience pillars for the RCIs of each governorate. While the evidence suggests that education variables within the AC pillar are the most important component of household resilience in all governorates, the roles of the other pillars (AST, ABS and SSN) in determining resilience capacity exhibit a large degree of variation between governorates.

0

20

40

60

80

100

Com

pone

nts

shar

e (%

)

Amman Balqa Zarqa Madaba Irbid Mafraq Jarash Ajlun Karak Tafiela Ma’an Aqaba

ABS

AST

SSN

AC

Figure 6. Share of RCI pillars by governorate

Assets (both productive and non-productive, agricultural and non-agricultural) are an important component of resilience capacity in all governorates, but their contribution to resilience is much higher in some governorates (Ma’an and Ajlun) than in others (Amman and Tafiela). However, despite this strong heterogeneity in terms of the importance of the AST pillar, this heterogeneity does not correspond to the division between the most resilient and least resilient governorates, and there is not a clear pattern that can be discerned to explain the variation of this pillar in determining the RCI.

There is a significant discrepancy in access to basic services across governorates. The figures for the ABS pillar (represented in blue in Figure 6) are very low in some governorates (Ma’an, Irbid, Jarash and Zarqa) and highest in Amman, the most populated and urbanized governorate; the most significant variable within the ABS pillar is safe and stable access to (public) water (see Figure A2 in Annex I). Despite being the most resilient governorate, Irbid likely has limited access to basic services relative to other, more urbanized governorates due to the fact it is located in the north of the country, where water stress and increased demand for water have increased as a consequence of the influx of Syrian refugees, according to the Jordanian government (MOPIC, 2017).

The SSN pillar is the lowest contributor to household food security in all governorates, irrespective of their level of resilience capacity. The only governorates where public transfers are a relevant driver of resilience capacity are Karak and Tafiela (which score below average in terms of resilience capacity – see Table A10); in the most resilient governorates – Irbid, Ajloun and Amman – the role of transfers in determining resilience capacity is very close to zero, due to the fact that households in these governorates are more resilient and thus there is less or no need for safety nets.


based on HEIS (2013)


16

2.3 HETEROGENEITY BY NATIONALITY OF HOUSEHOLD HEADHouseholds with a Jordanian HH are significantly more resilient than households with a non-Jordanian HH. Looking at the share of RCI pillars for these two categories (see Figure 8), the main sources of inequality between Jordanian-headed households and non-Jordanian-headed households are assets, which are lower for the latter, and transfers, which conversely are close to zero for the former and account for about 20 percent of the resilience capacity of non-Jordanian-headed households.

0

20

40

60

80

100

Com

pone

nts

shar

e (%

)

Jordanian HH Non Jordanian HH

ABS

AST

SSN

AC

Figure 8. Share of RCI pillar by nationality of the household head

This picture is very similar to the determinants of the heterogeneity detected for the household head gender gap; it appears transfers are crucial for non-Jordanian-headed households to at least partially compensate for the deficiencies in resilience capacity due to gaps in other pillars.

Due to representativeness constraints within the data employed for this analysis, analyses of specific nationalities (eg. Syrian) was not possible. However, it is still possible to investigate whether this heterogeneity between Jordanian-headed households and non-Jordanian-headed households also persists within each governorate or if it only exists on average across the whole of the country.

Figures 9 and 10 show the share of pillars of the RCI between Jordanian-headed households and non-Jordanian-headed households in two governorates: firstly, in Amman, the most populated governorate, which is also among the richest and most resilient governorates and arguably less affected by the impacts of the Syrian crisis; secondly, in Mafraq, a predominantly rural northern governorate that is among the least resilient regions and has received one of the highest numbers of refugees.

In contrast to the heterogeneity seen at the governorate level, the rural-urban resilience capacity gap is low (see Appendix 2, Table A10), which is consistent with findings from the United Nations Environment Programme (UNEP), which suggests that inequality between governorates is markedly higher than inequality between urban and rural areas (UNDP and UNICEF, 2015).

2.2. HETEROGENEITY BY GENDER OF HOUSEHOLD HEADFHHs are significantly less resilient than MHHs. Figure 7 below, which depicts the share of the RCI pillars for these two categories, sheds some light on the reasons for this discrepancy.

FHHs have lower access to basic services such as water and are on average less educated compared to MHHs. Conversely, assets seem to matter more for FHH resilience than for MHHs. There is evidence that transfers, especially public transfers and subsidies from the National Aid Fund (which are the key drivers of the SSN pillar – see Appendix I, Figure A4) partially compensate for this gap. This can be appreciated by looking at the discrepancy in the shares of the SSN pillar in Figure 7. For MHHs, transfers do not contribute to resilience capacity at all, suggesting that they have a relatively low need for safety nets, while for FHHs, transfers are almost as important in explaining resilience capacity as access to basic services. However, the increased role of transfers for FHHs is not enough to completely offset this resilience capacity inequality between HH genders.

These findings hold at the national level as well as at the sub-national one: the proportion of FHHs compared to MHHs is extremely similar across each governorate (with MHHs representing the vast majority of households overall) and there are no clusters in the distribution of FHHs in some governorates, which may be driving the results or the heterogeneity at the governorate level.

0

20

40

60

80

100

Com

pone

nts

shar

e (%

)

Male Female

ABS

AST

SSN

AC

Figure 7. Share of RCI pillars by gender of the household head



17


2.3 HETEROGENEITY BY NATIONALITY OF HOUSEHOLD HEADHouseholds with a Jordanian HH are significantly more resilient than households with a non-Jordanian HH. Looking at the share of RCI pillars for these two categories (see Figure 8), the main sources of inequality between Jordanian-headed households and non-Jordanian-headed households are assets, which are lower for the latter, and transfers, which conversely are close to zero for the former and account for about 20 percent of the resilience capacity of non-Jordanian-headed households.

0

20

40

60

80

100

Com

pone

nts

shar

e (%

)


ABS

AST

SSN

AC

Figure 8. Share of RCI pillar by nationality of the household head

This picture is very similar to the determinants of the heterogeneity detected for the household head gender gap; it appears transfers are crucial for non-Jordanian-headed households to at least partially compensate for the deficiencies in resilience capacity due to gaps in other pillars.

Due to representativeness constraints within the data employed for this analysis, analyses of specific nationalities (eg. Syrian) was not possible. However, it is still possible to investigate whether this heterogeneity between Jordanian-headed households and non-Jordanian-headed households also persists within each governorate or if it only exists on average across the whole of the country.

Figures 9 and 10 show the share of pillars of the RCI between Jordanian-headed households and non-Jordanian-headed households in two governorates: firstly, in Amman, the most populated governorate, which is also among the richest and most resilient governorates and arguably less affected by the impacts of the Syrian crisis; secondly, in Mafraq, a predominantly rural northern governorate that is among the least resilient regions and has received one of the highest numbers of refugees.

In contrast to the heterogeneity seen at the governorate level, the rural-urban resilience capacity gap is low (see Appendix 2, Table A10), which is consistent with findings from the United Nations Environment Programme (UNEP), which suggests that inequality between governorates is markedly higher than inequality between urban and rural areas (UNDP and UNICEF, 2015).

2.2. HETEROGENEITY BY GENDER OF HOUSEHOLD HEADFHHs are significantly less resilient than MHHs. Figure 7 below, which depicts the share of the RCI pillars for these two categories, sheds some light on the reasons for this discrepancy.

FHHs have lower access to basic services such as water and are on average less educated compared to MHHs. Conversely, assets seem to matter more for FHH resilience than for MHHs. There is evidence that transfers, especially public transfers and subsidies from the National Aid Fund (which are the key drivers of the SSN pillar – see Appendix I, Figure A4) partially compensate for this gap. This can be appreciated by looking at the discrepancy in the shares of the SSN pillar in Figure 7. For MHHs, transfers do not contribute to resilience capacity at all, suggesting that they have a relatively low need for safety nets, while for FHHs, transfers are almost as important in explaining resilience capacity as access to basic services. However, the increased role of transfers for FHHs is not enough to completely offset this resilience capacity inequality between HH genders.

These findings hold at the national level as well as at the sub-national one: the proportion of FHHs compared to MHHs is extremely similar across each governorate (with MHHs representing the vast majority of households overall) and there are no clusters in the distribution of FHHs in some governorates, which may be driving the results or the heterogeneity at the governorate level.

0

20

40

60

80

100

Com

pone

nts

shar

e (%

)

Male Female

ABS

AST

SSN

AC

Figure 7. Share of RCI pillars by gender of the household head




18

0

20

40

60

80

100

Com

pone

nts

shar

e (%

)


ABS

AST

SSN

AC

Figure 9. Share of RCI pillars by nationality of household head in Amman

0

20

40

60

80

100

Com

pone

nts

shar

e (%

)


ABS

AST

SSN

AC

Figure 10. Share of RCI pillars by nationality of household head in Mafraq

It is evident that heterogeneity between Jordanian-headed households and non-Jordanian-headed households is the case for both governorates. However, there are a few additional insights that emerge from this comparison that deserve to be mentioned.

First, the discrepancy between Jordanian-headed households and non-Jordanian-headed households for the ABS pillar, and consequently in access to water, is more evident for these two governorates compared to the national average.

Second, while non-Jordanian-headed households have fewer assets and rely much more on public transfers for their resilience capacity (consistent with the national picture depicted in Figure 8), there are significant regional disparities between Amman and Mafraq even between households





19


0

20

40

60

80

100

Com

pone

nts

shar

e (%

)


ABS

AST

SSN

AC

Figure 9. Share of RCI pillars by nationality of household head in Amman

0

20

40

60

80

100

Com

pone

nts

shar

e (%

)


ABS

AST

SSN

AC

Figure 10. Share of RCI pillars by nationality of household head in Mafraq

It is evident that heterogeneity between Jordanian-headed households and non-Jordanian-headed households is the case for both governorates. However, there are a few additional insights that emerge from this comparison that deserve to be mentioned.

First, the discrepancy between Jordanian-headed households and non-Jordanian-headed households for the ABS pillar, and consequently in access to water, is more evident for these two governorates compared to the national average.

Second, while non-Jordanian-headed households have fewer assets and rely much more on public transfers for their resilience capacity (consistent with the national picture depicted in Figure 8), there are significant regional disparities between Amman and Mafraq even between households

with the same HH nationality. Indeed, transfers matter much more for non-Jordanian-headed households than for Jordanian-headed households both in Amman and in Mafraq, however transfers matter more for Jordanian-headed households in Mafraq than for Jordanian-headed households in Amman (where households receive essentially no transfers). Similarly, transfers play a bigger role in boosting resilience capacity for non-Jordanian-headed households in Mafraq, where the SSN pillar accounts for approximately 40 percent of the total RCI for Mafraq, compared to the role of transfers for non-Jordanian-headed households in Amman. The same goes for ABS and water services, where both Jordanian and non-Jordanian-headed households in Mafraq are worse off than non-Jordanian-headed households in Amman. These results suggest that regional differences, rather than differences between the nationalities of household heads, are by far the most important source of resilience capacity inequality in Jordan.

h Resilience capacity inequality in Jordan should be addressed by national and international policymakers at multiple levels, with a particular focus on regional heterogeneities, as this is the most important driver of inequality in household resilience capacity.

h It is essential to improve water services and guarantee stable access to water in Ma’an and in the least resilient, northern governorates (Mafraq, Balqa, Zarqa) where water demand has increased due to the influx of Syrian refugees (MOPIC, 2017).

h Public transfers should predominantly target these least resilient governorates, as well as the least resilient categories, such as FFHs and non-Jordanian-headed households, so as to partially make up for the above-mentioned resilience capacity gaps.

h Specific policies on education for women should address inequality in access to education between genders.

h Investments should target the increase of the productive asset base of households in Jordan, and in particular of non-Jordanian-headed households.


MAIN FINDING 3Resilience to food insecurity of households in Jordan is strongly and negatively affected by food shortages, illness of household members and prolonged droughts.

Evidence for this finding is presented in Table A3, which shows the results of an Ordinary Least Squares (OLS) regression, which suggest that households in Jordan are vulnerable to both idiosyncratic shocks (shocks that only affect individuals or single households), such as food shortages9 and illness of household members, and to covariate shocks (which affect groups of households or even entire communities) such as droughts,10 especially if these are prolonged over time.

9 Measuring food shortages takes into account the number of days in a year in which members of the household did not eat food.

10 Droughts impacts are captured using anomalies in the NDVI and ASI. See Annex I, Causal Resilience Analysis Section for technical details.


20

© F

AO \ R

icca

rdo

Gan

gale

h In the medium term, UN agencies and international actors should provide technical support in the implementation phase of the JRP 2017-2019, in particular for the programme’s food security (Food Security sector, Specific Objective 1) and sanitation (WASH sector, Specific Objective 2) goals.

h From a long-term perspective, comprehensive interventions are needed to increase food stability at the household level by fostering the diversification of income sources at the household level, so as to reduce the vulnerability of households in Jordan not only to internal and environmental shocks, but also to exogenous stressors, such as the severe negative impact of the 2007-2008 global food price crisis11 (ESCWA, 2010), to which the country is heavily exposed given its heavy reliance on food imports.

h As for recurring exogenous shocks such as droughts, FAO and other actors should assist the Jordanian government in the preparation of contingency plans and early warning mechanisms and in the development of adaptation strategies for coping with the impacts of climate change at the household and community levels.


11 The 2007-2008 global food price crisis saw dramatic increases in the prices of most food commodities (wheat and rice in particular), and this in turn led to economic instability and social unrest in many countries. Given Jordan’s above-mentioned dependency on imports and its consequent exposure in international food markets, the country was particularly affected by the crisis and experienced soaring food prices, which led to protests and demonstrations.

© F

AO \ K

halil

Maz

raaw

i

© F

AO \ R

icca

rdo

Gan

gale

© F

AO \ K

halil

Maz

raaw

i

© F

AO \ M

arco

Let

ta

© F

AO \ S

ylva

in C

herk

aoui

© F

AO \ K

halil

Maz

raaw

i

23

© F

AO \ M

arco

Let

ta

4 METHODOLOGY AND COVERAGEThis section introduces the FAO RIMA-II approach employed for estimating household resilience capacity. This section describes the dataset employed in the resilience analysis, based on the HEIS 2013. Some data limitations are introduced as well.

This report defines resilience as “the capacity that ensures stressors and shocks do not have long-lasting development consequences” (RM-TWG, 2014). Building on this definition, resilience capacity is estimated through the FAO RIMA-II approach (FAO, 2016). The RIMA-II methodology employs both latent variable statistical techniques – for estimating the RCI and the four resilience pillars (ABS, AST, SSN and AC) at the household level – and regression models. Annex I provides details on the RIMA-II methodology.

The data employed for this RIMA-II analysis were obtained from two main sources – the household data are from the 2013 HEIS by the DoS,12 while data for climatic shocks, namely the Normalized Difference Vegetation Index (NDVI) and the Agricultural Stress Index (ASI) time series are from the FAO Global Information and Early Warning System (GIEWS) Database (FAO, 2017).

Food security generally depends on food availability, accessibility, sustainability and utilization (FAO, 2008). In this analysis, two variables have been employed to capture food security: food consumption per capita and the Food Consumption Score (FCS). FCS is a composite indicator that captures both the household variability of the diet (looking at the number of food groups consumed), food frequency (looking at the number of days each food item is consumed out of the last seven days prior to the interview) and the amount of calories consumed during the last seven days prior to the interview (each food group has a different weight depending on its specific caloric intake).

A limitation of the data used in this analysis is the non-representativeness of non-Jordanian-headed households in the sample. Indeed, this feature prevented a more in-depth analysis of resilience capacity for households belonging to specific nationalities (that are not Jordanian).13

12 As already stressed above, the RIMA-II econometric analysis was based on a partial sample of 7 898 households from the total of the 2013 HEIS survey, but the results of the analysis are representative thanks to the use of sample weights (included in the original dataset shared by the DoS) which have been applied to correct the estimates taking into account the probability of selection into sample.

13 This refers to the nationality of the entire household, based on the assumption that the nationality of the household head usually is representative of the nationality of all households members.

© F

AO \ K

halil

Maz

raaw

i


24

© F

AO \ K

halil

Maz

raaw

i

For Jordanian-headed households, the sample is representative. Thus, this only allowed for a broad comparison between Jordanian-headed households and non-Jordanian-headed households. There is a forthcoming data collection due to take place this year by the DoS, which will be the first HEIS survey to be representative for all nationalities in Jordan.

The main drawback of the analysis is represented by the fact that in using only this household survey, namely a cross-sectional dataset, it is only possible to observe resilience capacity in a single point in time (ie. in 2013). A dynamic approach would be particularly interesting and relevant from a policy point of view in a country such as Jordan, given the pressure on its socioeconomic system over the last few years caused by the huge influx of Syrian refugees fleeing the Syrian conflict. Unfortunately, exploring the dynamics of resilience capacity in Jordan over time is not possible using only this cross-sectional survey. Thus, this investigation of resilience dynamics and the related policy questions about the impact of the Syrian refugee crisis on household resilience in Jordan must be deferred in anticipation of a possible second wave of data from the HEIS surveys becoming available to FAO’s RAP team in future.

© F

AO \ K

halil

Maz

raaw

i

© F

AO \ M

arco

Let

ta

© F

AO \ S

ylva

in C

herk

aoui

© F

AO \ K

halil

Maz

raaw

i

27

© F

AO \ M

arco

Let

ta

5 NEXT STEPSThis section provides the criteria for prioritizing the recommendations resulting from stakeholder consultations. It includes the needs and time frame for action plans, further consultation, the development of a monitoring and evaluation framework, and the next RIMA-II analysis.

This analysis has provided an overview of the factors affecting households’ resilience capacity for coping with food insecurity in Jordan, and has highlighted significant levels of inequality in resilience capacity at multiple levels.

However, being a static exercise based on a 2013 dataset, this analysis should be seen as a promising starting point; it should be followed soon by other analyses investigating the evolution of resilience capacity over time, using further panel set data. It would be particularly useful to focus on the relationship between resilience capacity in Jordan and the huge socio-economic shock represented by the Syrian refugee crisis. Resilience capacity, in fact, is an intrinsically dynamic concept of which this analysis has only provided a snapshot.

In the meantime, the JRP 2017-2019 offers short-term solutions for the Jordanian government to address the most urgent needs and emergencies caused by the Syrian refugee crisis.

However, in order to address the aftermath of the Syrian refugee crisis in the long-term, a quantitative and evidence-based assessment of the determinants of resilience capacity and its dynamic relationship with shocks and stressors is urgently needed.

In light of the above-mentioned remarks, it is particularly recommended to:

h reinforce the technical collaboration between FAO and the Jordanian DoS through the support of RFSAN and the FAO Jordan office;

h repeat the RIMA-II econometric analysis on future HEIS datasets, and compare the new findings with the core results emerging from this report;

h incorporate different HEIS surveys in a single unified dataset covering a time span of several years, in order to explore the dynamics of resilience capacity by exploiting either repeated cross-section data sets or longitudinal data, and focus on the causal relationship between the evolution of household resilience capacity in Jordan and the shock represented by the Syrian refugee crisis; and

© F

AO \ K

halil

Maz

raaw

i


28

h integrate the FAO RIMA-II methodology with the resilience approach of the Jordanian government and other international actors in the country in order to provide a comprehensive framework for studying the determinants of household resilience capacity, and to promote a more quantitative and evidence-based approach to resilience capacity measurement and analysis in Jordan.

29

REFERENCES

DoS. 2016. The State of Food Security in Jordan (2013-2014) – Analytical Report [online]. Jordan. [Cited 5 December 2017]. http://documents.wfp.org/stellent/groups/public/documents/ena/wfp283421.pdf

ESCWA. 2010. Food Security and Conflict in the ESCWA Region [online]. New York, USA. [Cited 5 December 2017]. https://www.unescwa.org/sites/www.unescwa.org/files/publications/files/e_escwa_ecri_10_1_e.pdf

FAO. 2008. An Introduction to the Basic Concepts of Food Security [online]. [Cited 4 December 2017]. http://www.fao.org/docrep/013/al936e/al936e00.pdf

FAO. 2016. RIMA-II: Resilience Index Measurement and Analysis II [online]. [Cited 4 December 2017]. www.fao.org/3/a-i5665e.pdf

FAO. 2017. Data & Tools. In: FAO [online]. [Cited 5 December 2017]. http://www.fao.org/giews/data-tools/en/

MOPIC. 2017. Jordan Response Plan for the Syria Crisis 2017-2019 – Executive Summary [online]. Jordan. [Cited 5 December 2017]. https://static1.squarespace.com/static/522c2552e4b0d3c39ccd1e00/t/595689aef5e231fdd546f0ad/1498843601005/Executive+Summary+-+%28June+30%29.pdf

RM-TWG. 2014. Resilience Measurement Principles – Toward an agenda for measurement design [online]. Rome, Italy. [Cited 4 December 2017]. www.fsincop.net/fileadmin/user_upload/fsin/docs/resources/FSIN_29jan_WEB_medium%20res.pdf

UNDP. 2017. Table 1: Human Development Index and its components. In: UNDP Human Development Reports [online]. [Cited 5 December 2017]. http://hdr.undp.org/en/composite/HDI

UNDP & UNICEF. 2015. Socio-economic inequality in Jordan [online]. In: UNDP Jordan [Cited 4 December 2017] http://www.jo.undp.org/content/jordan/en/home/library/poverty/socio-economic-inequality-in-jordan-report.html

UNRWA. 2017. Where we work. In: UNRWA [online]. [Cited 5 December 2017]. https://www.unrwa.org/where-we-work/jordan

WFP. 2017. WFP Jordan – Country Brief [online]. Jordan. [Cited 4 December 2017]. http://documents.wfp.org/stellent/groups/public/documents/ep/wfp274960.pdf?_ga=2.70344784.1903427350.1504790217-446279595.14992563


30

World Bank. 2016. Jordan - Country partnership framework for the period FY17-22 [online]. [Cited 4 December 2017] http://documents.worldbank.org/curated/en/528081467900685594/Jordan-Country-partnership-framework-for-the-period-FY17-22

World Bank. 2017a. Population, total. In: The World Bank [online]. [Cited 5 December 2017]. https://data.worldbank.org/indicator/SP.POP.TOTL?locations=JO

World Bank. 2017b. Urban Population (% of total). In: The World Bank [online]. [Cited 5 December 2017]. https://data.worldbank.org/indicator/SP.URB.TOTL.IN.ZS?locations=JO

World Bank. 2017c. Jordan’s Economic Outlook – April 2017. In: The World Bank [online]. [Cited 4 December 2017].

http://www.worldbank.org/en/country/jordan/publication/economic-outlook-april-2017

World Bank. 2017d. Jordan. In: The World Bank [online]. [Cited 5 December 2017]. https://data.worldbank.org/country/jordan

All links were checked on 5 December 2017.

31

ANNEX IRESILIENCE MEASUREMENT

Resilience is a latent variable that cannot be directly measured.

In this study, resilience measurement is carried out in the context of a framework provided by the FAO, under a tool called Resilience Index Measurement and Analysis-II (RIMA-II).

This tool estimates resilience following the definition of resilience as provided by the Resilience Measurement Technical Working Group (RM-TWG):14 “the capacity that ensures adverse stressors and shocks do not have long-lasting adverse development consequences”.

Following the RIMA-II approach (FAO, 2016), the estimation of the RCI is based on a two-stage procedure.

1. First, the resilience pillars are estimated from observed variables through Factor Analysis (FA).

2. Second, the RCI is estimated from the pillars, taking into account the indicators of food security using the Multiple Indicators Multiple Causes (MIMIC) model. The food security indicators are considered outcomes of resilience.

The definitions of each pillar of resilience and the related variables are reported in Table A1 and A2. The choice of the employed variables for each pillar is based on consultations with the RFSAN team, the DoS and other local experts, literature review and previous analyses (FAO, 2016).

14 The RM-TWG has been established under the Food Security Information Network.


32

Table A1. Definition of resilience pillars and food security indicators

Definition VariablesPillars

of resilience

ABS

ABS shows the ability of a household to meet basic needs by accessing and effectively using basic services, such as sending children to school; accessing water, electricity and sanitation; selling products at the market.

Dwelling index; service closeness; service satisfaction; stable water; public water; daily water supply

AST

AST reflects assets, both productive and non-productive, which are the key elements of a livelihood since they enable households to produce and consume goods. Examples of productive assets include the land and agricultural index (eg. agricultural equipment), while non-agricultural assets take into account the monetary value of the house where the household is located, and its appliances.

Wealth index (per capita); animal index (per capita); land lots; tools and equipment for production; residential buildings for household use

SSNSSN proxies the ability of the household to access formal and informal financial assistance from institutions, as well as from relatives and friends.

Public transfers; private transfers; subsidies from the National Aid Fund (NAF)

AC

AC reflects adaptive capacity, or the ability to adapt to a new situation and develop new livelihood strategies. For instance, proxies of AC are the average years of education of household members and the household’s perception of the decision-making process of their community.

Average years of education of household members; education level of the household head; dependency ratio (inverse); participation index

Food securityFood consumed including food that is bought, produced by the household and received for free.

Food consumption per capita

Dietary diversity, food frequency, and relative nutritional importance of different food groups.

FCS

Table A2. Variables emloyed in the RIMA-II model

Pillar Variable Definition

ABS

Dwelling index

The dwelling index is created through FA. A list of variables including the area (m2) of the dwelling, number of rooms, monthly rental value of the dwelling, whether the kitchen is inside the dwelling or not, and others, is used to build this index.

Service closeness

Service closeness is created through FA. It indicates the degree of closeness (from 0 to 1) to services such as education, health and security, taking into account the distances from households to schools, hospitals and police stations.

Service satisfactionService satisfaction is created through FA and ranges from 0 to 1. It includes household self-reported satisfaction level with regard to public services such as water, health and transport.

Stable water Dummy variable taking a value of 1 if a household experienced no water shortages in the last 12 months, regardless of the source (public or not).

Public water Dummy variable taking a value of 1 if public water was the main source of water for the household in the last 12 months, and taking a value of 0 otherwise.

Daily public water supply Dummy variable taking a value of 1 if a household had daily access to public water in the last 12 months and 0 otherwise.

AST

Wealth index (per capita)

The wealth index is created through FA, using dummy variables that capture whether or not a household owns specific non-productive physical assets, such as a TV, freezer, microwave, computer, fax, air conditioner, water filter, sewing machine, car and others.

Animal index (per capita)The animal index is created through FA, using dummy variables capturing whether the household owns cows, sheep, poultry or other birds, beehives and other animals.

Land lots Dummy variable taking a value of 1 if a household owns land lots and 0 otherwise.

Tools and equipment for production

Dummy variable taking a value of 1 if a household has tools and equipment for production purposes and 0 otherwise.

Residential buildings for household use

Dummy variable taking a value of 1 if a household owns residential buildings for household use and 0 otherwise.

SSN

Public transfers Average monthly amount (USD) of public transfers received by the household in the last 12 months.

Private transfers Average monthly amount (USD) of private transfers received by the household in the last 12 months.

Subsidies from the National Aid Fund (NAF)

Dummy variable taking a value of 1 if a household received subsidies from the NAF in the last 12 months and 0 otherwise.

AC

Average education Average years of education of household members.Education of the household head Years of education of the household head.

Dependency ratio (inverse)

This variable is the inverse value of the dependency ratio. The dependency ratio is the ratio between the number of household members of working age and the other members of the household.

Participation index

The participation index accounts for income diversification. It ranges from 0 to 1 and is created through FA. The variables used to build this index capture all the income-generating activities in which the household is involved.

Food Security


Monetary value (USD) of monthly per capita food consumption, including food that is bought, auto-produced, and received for free.

Food Consumption Score (FCS)

Indicator that captures household diet variability, food frequency and the amount of calories consumed during the last seven days before the interview (each food group has a different weight depending on its specific caloric intake).

33

Annex I – Resilience measurement

Table A1. Definition of resilience pillars and food security indicators

Definition VariablesPillars

of resilience

ABS

ABS shows the ability of a household to meet basic needs by accessing and effectively using basic services, such as sending children to school; accessing water, electricity and sanitation; selling products at the market.

Dwelling index; service closeness; service satisfaction; stable water; public water; daily water supply

AST

AST reflects assets, both productive and non-productive, which are the key elements of a livelihood since they enable households to produce and consume goods. Examples of productive assets include the land and agricultural index (eg. agricultural equipment), while non-agricultural assets take into account the monetary value of the house where the household is located, and its appliances.

Wealth index (per capita); animal index (per capita); land lots; tools and equipment for production; residential buildings for household use

SSNSSN proxies the ability of the household to access formal and informal financial assistance from institutions, as well as from relatives and friends.

Public transfers; private transfers; subsidies from the National Aid Fund (NAF)

AC

AC reflects adaptive capacity, or the ability to adapt to a new situation and develop new livelihood strategies. For instance, proxies of AC are the average years of education of household members and the household’s perception of the decision-making process of their community.

Average years of education of household members; education level of the household head; dependency ratio (inverse); participation index

Food securityFood consumed including food that is bought, produced by the household and received for free.


Dietary diversity, food frequency, and relative nutritional importance of different food groups.

FCS

Table A2. Variables emloyed in the RIMA-II model

Pillar Variable Definition

ABS

Dwelling index

The dwelling index is created through FA. A list of variables including the area (m2) of the dwelling, number of rooms, monthly rental value of the dwelling, whether the kitchen is inside the dwelling or not, and others, is used to build this index.

Service closeness

Service closeness is created through FA. It indicates the degree of closeness (from 0 to 1) to services such as education, health and security, taking into account the distances from households to schools, hospitals and police stations.

Service satisfactionService satisfaction is created through FA and ranges from 0 to 1. It includes household self-reported satisfaction level with regard to public services such as water, health and transport.

Stable water Dummy variable taking a value of 1 if a household experienced no water shortages in the last 12 months, regardless of the source (public or not).

Public water Dummy variable taking a value of 1 if public water was the main source of water for the household in the last 12 months, and taking a value of 0 otherwise.

Daily public water supply Dummy variable taking a value of 1 if a household had daily access to public water in the last 12 months and 0 otherwise.

AST

Wealth index (per capita)

The wealth index is created through FA, using dummy variables that capture whether or not a household owns specific non-productive physical assets, such as a TV, freezer, microwave, computer, fax, air conditioner, water filter, sewing machine, car and others.

Animal index (per capita)The animal index is created through FA, using dummy variables capturing whether the household owns cows, sheep, poultry or other birds, beehives and other animals.

Land lots Dummy variable taking a value of 1 if a household owns land lots and 0 otherwise.

Tools and equipment for production

Dummy variable taking a value of 1 if a household has tools and equipment for production purposes and 0 otherwise.

Residential buildings for household use

Dummy variable taking a value of 1 if a household owns residential buildings for household use and 0 otherwise.

SSN

Public transfers Average monthly amount (USD) of public transfers received by the household in the last 12 months.

Private transfers Average monthly amount (USD) of private transfers received by the household in the last 12 months.

Subsidies from the National Aid Fund (NAF)

Dummy variable taking a value of 1 if a household received subsidies from the NAF in the last 12 months and 0 otherwise.

AC

Average education Average years of education of household members.Education of the household head Years of education of the household head.

Dependency ratio (inverse)

This variable is the inverse value of the dependency ratio. The dependency ratio is the ratio between the number of household members of working age and the other members of the household.

Participation index

The participation index accounts for income diversification. It ranges from 0 to 1 and is created through FA. The variables used to build this index capture all the income-generating activities in which the household is involved.

Food Security


Monetary value (USD) of monthly per capita food consumption, including food that is bought, auto-produced, and received for free.

Food Consumption Score (FCS)

Indicator that captures household diet variability, food frequency and the amount of calories consumed during the last seven days before the interview (each food group has a different weight depending on its specific caloric intake).


34

Table A3. MIMIC regression results

ABS 0.025(7.36)**

AST 0.026(9.28)**

SSN -0.001(1.51)

AC 0.060(11.25)**

Food expenditure usd (log) 1

FCS 48.621(12.01)**

N 7 898

Chi2 27.848

Prob > Chi2 0.000

CFI 0.985

TLI 0.955

RMSEA 0.032

RCI 60.059

St.dev. 15.491

Min 0

Max 100* p<0.05; ** p<0.01

The descriptive resilience analysis provides a description of household resilience capacity; it estimates the RCI and the Resilience Structure Matrix (RSM). The latter shows the correlation between the RCI and the pillars and between the observed variables and the pillars.

Figures A2-A4 show the correlations between the ABS, AST and SSN pillars and their respective variables.

Finally, Appendix II provides descriptive statistics for all the pillar variables (Table A9) and for the RCI by categories and geographic areas (Table A10).

After estimating the pillars, the RCI is estimated through an SEM, known as the MIMIC model.

MIMIC estimates resilience as determined by the four pillars; food security indicators are adopted as a measure of the outcome of resilience (ie. higher food security corresponds to higher resilience capacity).

Figure A1 presents a graphical representation the two-stage procedure for RCI estimation.

Resilience

ABS

εv1

v1

εv2

v2

...

...

εvn

vn

AST

εv1

v1

εv2

v2

...

...

εvn

vn

SSN

εv1

v1

εv2

v2

...

...

εvn

vn

AC

εv1

v1

εv2

v2

...

...

εvn

vn

ε1

Food consumption

per capitaSimpson

DDI FCS

ε4ε2 ε3

Observed variables ErrorsLatent variables

FAM

IMIC

Figure A1. RIMA-II Methodology

After estimating the RCI, a min-max scaling is used to transform the RCI value into a standardized index, ranging between 0 and 100. The linear scaling is based on:

RCI * = (RCI - RCImin ) / (RCImax - RCImin ) (1)

The results of the MIMIC model are shown in Table A3.

The model presents an overall good fit of the data; in the case of Jordan, all the pillars’ coefficients are positive and statistically significant with the exception of SSN, which is negative, close to zero and statistically insignificant.



35


Table A3. MIMIC regression results

ABS 0.025(7.36)**

AST 0.026(9.28)**

SSN -0.001(1.51)

AC 0.060(11.25)**

Food expenditure usd (log) 1

FCS 48.621(12.01)**

N 7 898

Chi2 27.848

Prob > Chi2 0.000

CFI 0.985

TLI 0.955

RMSEA 0.032

RCI 60.059

St.dev. 15.491

Min 0

Max 100* p<0.05; ** p<0.01

The descriptive resilience analysis provides a description of household resilience capacity; it estimates the RCI and the Resilience Structure Matrix (RSM). The latter shows the correlation between the RCI and the pillars and between the observed variables and the pillars.

Figures A2-A4 show the correlations between the ABS, AST and SSN pillars and their respective variables.

Finally, Appendix II provides descriptive statistics for all the pillar variables (Table A9) and for the RCI by categories and geographic areas (Table A10).

After estimating the pillars, the RCI is estimated through an SEM, known as the MIMIC model.

MIMIC estimates resilience as determined by the four pillars; food security indicators are adopted as a measure of the outcome of resilience (ie. higher food security corresponds to higher resilience capacity).

Figure A1 presents a graphical representation the two-stage procedure for RCI estimation.

Resilience

ABS

εv1

v1

εv2

v2

...

...

εvn

vn

AST

εv1

v1

εv2

v2

...

...

εvn

vn

SSN

εv1

v1

εv2

v2

...

...

εvn

vn

AC

εv1

v1

εv2

v2

...

...

εvn

vn

ε1

Food consumption

per capitaSimpson

DDI FCS

ε4ε2 ε3

Observed variables ErrorsLatent variables

FAM

IMIC

Figure A1. RIMA-II Methodology

After estimating the RCI, a min-max scaling is used to transform the RCI value into a standardized index, ranging between 0 and 100. The linear scaling is based on:

RCI * = (RCI - RCImin ) / (RCImax - RCImin ) (1)

The results of the MIMIC model are shown in Table A3.

The model presents an overall good fit of the data; in the case of Jordan, all the pillars’ coefficients are positive and statistically significant with the exception of SSN, which is negative, close to zero and statistically insignificant.


36

Daily water Supply

Public Water

Service Satisfaction

Dwelling index

Service Closeness

Stable Water

Figure A2. Share of ABS variables in Jordan

Animal Index

Res. Buildings

Wealth Index

Land Lots

Tools

Figure A3. Share of AST variables in Jordan

NAF Subsidies

Public Transfers

Private Transfers

Figure A4. Share of SSN variables in Jordan

To investigate the association between shocks and the RCI, the following regression model is employed:

RCIh = α + βSh + γCd + δXh + εh (2)

where Sh are dummy variables for self-reported shocks at the household level; Cd is a variable that captures an environmental covariate shock, represented alternatively by the level of NDVI/ASI anomaly15 at the district level; Xh are control characteristics; εh is the error term and RCIh is the rescaled (0-100) RCI estimated through the RIMA-II model.

Table A4 shows the results of regression model (2); Tables A5 and A6 report the estimates of the same model adopting alternative outcome variables for total food consumption and the FCS, respectively.

Additionally, in separated regressions, the food security indicators (food consumption and FCS) employed for estimating the RCI and the variables used for estimating the pillars are used as the outcome of interest. These results are reported in Tables A7 (for food consumption) and A8 (for the FCS).

Such causal analyses are limited by the cross-sectional nature of the data, which implies that these findings could well be affected by omitted variable bias and a range of potential confounding factors.

Of course, the availability of other HEIS data sets would also allow a more thorough explanation of the role of shocks and their impacts on resilience capacity over time and provide a more robust causal analysis of this relationship.

15 Calculated both as the difference between the average NDVI/ASI in 2013 and the long-term average, divided by the long-run standard deviation; to investigate persistence of shocks, this was also calculated as the difference between the average NDVI/ASI in a five-year period (2009-2013) and the long-term average, divided by the long-run standard deviation.





37


Daily water Supply

Public Water

Service Satisfaction

Dwelling index

Service Closeness

Stable Water

Figure A2. Share of ABS variables in Jordan

Animal Index

Res. Buildings

Wealth Index

Land Lots

Tools

Figure A3. Share of AST variables in Jordan

NAF Subsidies

Public Transfers

Private Transfers

Figure A4. Share of SSN variables in Jordan

To investigate the association between shocks and the RCI, the following regression model is employed:

RCIh = α + βSh + γCd + δXh + εh (2)

where Sh are dummy variables for self-reported shocks at the household level; Cd is a variable that captures an environmental covariate shock, represented alternatively by the level of NDVI/ASI anomaly15 at the district level; Xh are control characteristics; εh is the error term and RCIh is the rescaled (0-100) RCI estimated through the RIMA-II model.

Table A4 shows the results of regression model (2); Tables A5 and A6 report the estimates of the same model adopting alternative outcome variables for total food consumption and the FCS, respectively.

Additionally, in separated regressions, the food security indicators (food consumption and FCS) employed for estimating the RCI and the variables used for estimating the pillars are used as the outcome of interest. These results are reported in Tables A7 (for food consumption) and A8 (for the FCS).

Such causal analyses are limited by the cross-sectional nature of the data, which implies that these findings could well be affected by omitted variable bias and a range of potential confounding factors.

Of course, the availability of other HEIS data sets would also allow a more thorough explanation of the role of shocks and their impacts on resilience capacity over time and provide a more robust causal analysis of this relationship.

15 Calculated both as the difference between the average NDVI/ASI in 2013 and the long-term average, divided by the long-run standard deviation; to investigate persistence of shocks, this was also calculated as the difference between the average NDVI/ASI in a five-year period (2009-2013) and the long-term average, divided by the long-run standard deviation.




38

Table A4. RCI and shocks Table A5. Food expenditure and shocks

Dependent variable(1) (2) (3) (4)RCI RCI RCI RCI

ASI shock annual -0.371***(0.0660)

Food shock -19.88*** -20.10*** -19.90*** -20.00***(3.767) (3.768) (3.764) (3.785)

Job loss shock -2.414 -2.445 -2.467 -2.373(1.538) (1.539) (1.540) (1.529)

Illness shock -4.082*** -3.997*** -4.045*** -4.064***(1.108) (1.096) (1.103) (1.101)

Non-Jordanian HH -1.651 -1.820 -1.625 -1.825(1.276) (1.268) (1.276) (1.263)

Household size 14.29*** 14.13*** 14.33*** 14.05***(2.212) (2.195) (2.211) (2.187)

(Household size)2 -3.870*** -3.779*** -3.875*** -3.773***(0.772) (0.768) (0.773) (0.763)

Age of the HH 0.0219 0.0192 0.0211 0.0202(0.0222) (0.0222) (0.0222) (0.0222)

Gender of the HH -2.641*** -2.585*** -2.597*** -2.637***(0.844) (0.836) (0.842) (0.839)

Rural dummy -2.389*** -1.827*** -1.937*** -2.219***(0.471) (0.469) (0.471) (0.482)

Number of infants -0.262 -0.233 -0.259 -0.245(0.355) (0.356) (0.356) (0.355)

Income 0.0196*** 0.0196*** 0.0197*** 0.0195***(0.00433) (0.00435) (0.00434) (0.00432)

NDVI shock annual 1.645*(0.890)

NDVI shock (5 years) 3.585***(0.763)

ASI shock (5 years) -0.535***(0.125)

Constant 48.96*** 47.96*** 47.26*** 48.93***(3.096) (3.100) (3.110) (3.082)

Observations 7 898 7 898 7 898 7 898R-squared 0.132 0.126 0.130 0.129

Robust standard errors in parentheses.*** p<0.01, ** p<0.05, * p<0.1

39


Table A4. RCI and shocks Table A5. Food expenditure and shocks

Dependent variable(1) (2) (3) (4)

Food expenditure

Food expenditure

Food expenditure

Food expenditure

ASI shock annual -0.00374*(0.00219)

Food shock -0.378*** -0.388*** -0.386*** -0.376***(0.103) (0.103) (0.103) (0.103)

Job loss shock -0.108** -0.108** -0.108** -0.106**(0.0526) (0.0522) (0.0523) (0.0524)

Illness shock -0.116*** -0.112*** -0.113*** -0.117***(0.0337) (0.0335) (0.0336) (0.0339)

Non-Jordanian HH -0.0396 -0.0492 -0.0470 -0.0396(0.0398) (0.0395) (0.0395) (0.0398)

Household size 0.203** 0.193** 0.195** 0.201**(0.0886) (0.0879) (0.0880) (0.0886)

(Household size)2 -0.223*** -0.218*** -0.219*** -0.222***(0.0288) (0.0285) (0.0285) (0.0288)

Age of the HH 0.00213*** 0.00202*** 0.00204*** 0.00214***(0.000769) (0.000765) (0.000766) (0.000769)

Gender of the HH -0.103*** -0.102*** -0.102*** -0.103***(0.0280) (0.0278) (0.0279) (0.0281)

Rural dummy -0.000638 0.0157 0.0108 -0.00613(0.0152) (0.0151) (0.0150) (0.0154)

Number of infants -0.0754*** -0.0742*** -0.0744*** -0.0756***(0.0113) (0.0112) (0.0113) (0.0113)

Income 0.000746*** 0.000741*** 0.000743*** 0.000744***(0.000156) (0.000156) (0.000156) (0.000156)

NDVI shock annual -0.0744**(0.0290)

NDVI shock 5 years -0.0450*(0.0250)

ASI shock 5 years -0.0120***(0.00414)

Constant 4.639*** 4.643*** 4.644*** 4.648***(0.110) (0.109) (0.110) (0.110)




40

Table A6. FCS and shocks Table A7. Food expenditure and pillar variables

Dependent variable(1) (2) (3) (4)

FCS FCS FCS FCS

ASI shock annual -0.320***(0.0548)

Food shock -16.11*** -16.27*** -16.09*** -16.22***(3.137) (3.135) (3.130) (3.153)

Job loss shock -1.352 -1.379 -1.399 -1.321(1.257) (1.259) (1.258) (1.249)

Illness shock -2.961*** -2.896*** -2.937*** -2.939***(0.898) (0.889) (0.895) (0.892)

Non-Jordanian HH -1.044 -1.160 -0.991 -1.202(1.043) (1.037) (1.045) (1.030)

Household size 9.728*** 9.621*** 9.795*** 9.517***(1.827) (1.817) (1.829) (1.804)

(Household size)2 -2.142*** -2.077*** -2.160*** -2.054***(0.647) (0.646) (0.649) (0.639)

Age of the HH 0.0293 0.0272 0.0289 0.0276(0.0180) (0.0180) (0.0180) (0.0179)

Gender of the HH -1.645** -1.598** -1.608** -1.637**(0.697) (0.691) (0.696) (0.692)

Rural dummy -1.817*** -1.371*** -1.454*** -1.635***(0.391) (0.390) (0.391) (0.399)

Number of infants 0.105 0.127 0.104 0.122(0.291) (0.291) (0.291) (0.290)

Income 0.0131*** 0.0131*** 0.0131*** 0.0130***(0.00290) (0.00292) (0.00291) (0.00289)

NDVI shock annual 1.751**(0.740)

NDVI shock 5 years 3.413***(0.634)

ASI shock 5 years -0.429***(0.104)

Constant 72.06*** 71.14*** 70.50*** 71.98***(2.371) (2.375) (2.386) (2.355)



41


Table A6. FCS and shocks Table A7. Food expenditure and pillar variables

Dependent variable(1)

Food expenditure (log)

Dwelling index 0.108***(0.0148)

Service closeness -0.00296(0.0322)

Service satisfaction 0.0726**(0.0291)

Daily public water supply -0.0101(0.0406)

Stable water 0.00309(0.0180)

Access to public water 0.0804**(0.0391)

Participation index 0.381***(0.0819)

Dependency ratio (inverse) -0.0658***(0.0121)

Average education 0.00838**(0.00395)

Education of the HH 0.00513*(0.00278)

Wealth index (per capita) 0.252**(0.114)

Residential buildings -0.147***(0.0294)

Animal index (per capita) 0.0365*(0.0191)

Land lots 0.103***(0.0222)

Tools and equipment 0.0235(0.0508)

Public transfers 0.000130***(3.08e-05)

Private transfers -3.55e-05(8.01e-05)

Subsidies from NAF -0.173***(0.0315)

Non-Jordanian HH 0.0120(0.0386)

Household size -0.165*(0.0975)

(Household size)2 -0.136***(0.0308)

Age of the HH 0.000712(0.000802)

Gender of the HH -0.0595**(0.0285)

Rural 0.0245(0.0151)

Number of infants -0.0232**(0.0111)

Income 0.000327***(7.87e-05)

Constant 4.756***(0.111)

Observations 7 898R-squared 0.421

Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1


42

ANNEX IIDESCRIPTIVE STATISTICS

Table A9. Pillar variables

Table A8. FCS and pillar variables

Dependent variable(1)

FCS

Dwelling index 2.558***(0.374)

Service closeness 1.564**(0.746)

Service satisfaction 1.547**(0.775)

Daily public water supply -1.153(0.986)

Stable water 1.423***(0.456)

Access to public water 1.494(1.070)

Participation index 5.702***(2.031)

Dependency ratio (inverse) -0.0932(0.313)

Average education 0.387***(0.0926)

Education of the HH 0.154**(0.0742)

Wealth index (per capita) -1.097(2.017)

Residential buildings -1.247*(0.737)

Animal index (per capita) -0.499(0.785)

Land lots 2.103***(0.481)

Tools and equipment -0.623(1.304)

Public transfers 0.00211***(0.000653)

Private transfers 0.00676***(0.00180)

Subsidies from NAF -4.793***(0.862)

Non-Jordanian HH -1.076(1.015)

Household size 2.447(1.969)

(Household size)2 -0.339(0.686)

Age of the HH 0.0230(0.0187)

Gender of the HH -0.862(0.710)

Rural -0.316(0.412)

Number of infants 0.760**(0.305)

Income 0.00322***(0.00115)

Constant 67.51***(2.389)

Observations 7 898R-squared 0.165

Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1

43

Annex II– Descriptive statistics

ANNEX IIDESCRIPTIVE STATISTICS

Table A9. Pillar variables

Table A8. FCS and pillar variables

Mean Var sd min max Count

Dwelling index -0.123 0.580 0.761 -3.923 8.217 7 898Service closeness 0.180 0.062 0.248 0 1 7 898Service satisfaction 0.105 0.056 0.237 0 1 7 898Stable water 0.248 0.186 0.432 0 1 7 898Public water 0.960 0.039 0.196 0 1 7 898Daily public water supply 0.040 0.039 0.196 0 1 7 898Participation index 0.560 0.023 0.153 0 1 7 898Dependency ratio (inv) 1.841 0.665 0.816 1 7.000 7 898Average education 9.245 14.16 3.764 0 20.50 7 898Education of the HH 9.032 23.48 4.846 0 24 7 898Residential buildings 0.793 0.164 0.405 0 1 7 898Wealth index (pc) -0.031 0.020 0.143 -1.281 1.078 7 898Animal index (pc) 0.024 0.120 0.347 -0.193 10.40 7 898Land lots 0.211 0.166 0.408 0 1 7 898Tools and equipment 0.019 0.018 0.135 0 1 7 898Public transfers (USD) 178.8 67 281.2 259.4 0 4 245.1 7 898Private transfers (USD) 43.69 10 852.5 104.2 0 2 364.1 7 898Subsidies from NAF 0.075 0.069 0.263 0 1 7 898


44

Table A10. RCI heterogeneity

RCI AverageAggregate 60.059Urban 60.914Rural 58.595Amman 60.598Balqa 54.738Zarqa 54.179Madaba 60.968Irbid 68.314Mafraq 54.680Jarash 57.604Ajlun 67.035Karak 59.929Tafiela 56.859Ma’an 61.329Aqaba 56.838FHH 55.732MHH 60.738Jordanian-headed household 60.252Non-Jordanian-headed household 56.004

Table A10. RCI heterogeneity

Graphic designer: Tomaso Lezzi

This document fits into the series of country level analyses prepared by the FAO Resilience Analysis and Policies (RAP) Team.

The series aims at providing programming and policy guidance to policy makers, practitioners, UN agencies, NGOs and other stakeholders by identifying the key factors that contribute to the resilience of households in food insecure countries and regions.

The analysis is largely based on the use of the FAO Resilience Index Measurement and Analysis (RIMA) tool. Structural Equation Models are applied to estimate resilience capacity and structure.

Contacts: Luca Russo, FAO Senior Economist - [email protected] Marco d’Errico, FAO Economist - [email protected]

The Food and Agriculture Organization of the United Nations (FAO)would like to thank the European Union for the financial supportwhich made possible the development of this publication.

11 N - Food and Agriculture Organization1 The resilience pillars form part of the RIMA-II...

Documents

Transcript of 11 N - Food and Agriculture Organization1 The resilience pillars form part of the RIMA-II...