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Food and Agriculture Organization of the United NationsRome, 2017

KAramoja 2016

KARAMOJA REGION (UGANDA), BASELINE REPORT FOR IMPACT EVALUATION OF FAO-UNICEF-WFP RESILIENCE PROGRAMMING

FAO baseline IMPACT EVALUATION report No. 2

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 978-92-5-109995-7

© FAO, 2017

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KARAMOJA REGION (UGANDA), Baseline report for impact evaluation of FAO-UNICEF-WFP resilience programming

CONTENTS

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

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

1 INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

2 THEORY OF CHANGE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 IMPACT EVALUATION STRATEGY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74 DATA COLLECTION STRATEGY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115 SAMPLING STRATEGY AT THE BASELINE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136 BALANCE TEST . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 177 CONCLUSIONS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

REFERENCES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

ANNEX I – ESTIMATION OF RCI AND RESILIENCE PILLARS THROUGH RIMA-II

METHODOLOGY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

ANNEX II – SAMPLING WEIGHTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

ANNEX III – REGRESSION MODELS: PROBABILITY OF BEING A TREATMENT GROUP HOUSEHOLD VERSUS HOUSEHOLD FROM A DIFFERENT STRATUM . . . . . . . . . . . . . . . . . 32

ANNEX IV – SUMMARY STATISTICS: MEAN VALUES OF VARIABLES BY SAMPLING STRATUM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

FIGURESFig. 1 Uganda and Karamoja (by district) maps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1Fig. 2 ToC of the JRS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4Fig. 3 Sampling strata map . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15Fig. A1 Distribution of the propensity score: treatment group household vs pure control group household . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34Fig. A2 Distribution of the propensity score: treatment group household vs direct spillover group household . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37Fig. A3 Distribution of the propensity score: treatment group household vs indirect spillover group household . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40Fig. A4 Distribution of the propensity score: treatment group household vs different ethnicity group household . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

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TABLESTab. 1 Number of interviewed households by district and sampling stratum . . . . . . . . . . . . 15Tab. 2 Mean RCI by sampling stratum . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18Tab. 3 Mean ABS by sampling stratum . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19Tab. 4 Mean AST by sampling stratum . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19Tab. 5 Mean SSN by sampling stratum . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20Tab. 6 Mean AC by sampling stratum . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20Tab. 7 Resilience variables by sampling stratum . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21Tab. A1 Variables emloyed in the RIMA model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27Tab. A2 MIMIC results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29Tab. A3 Sampling weights at subcounty level . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30Tab. A4 Linear Probability Model (LPM) and Probit model of being treatment group household vs pure control group household . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32Tab. A5 Linear Probability Model (LPM) and Probit model of being treatment group household vs direct spillover group household . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35Tab. A6 Linear Probability Model (LPM) and Probit model of being a treatment group household vs indirect spillover group household . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38Tab. A7 Linear Probability Model (LPM) and Probit model of being a treatment group household vs different ethnicity group household . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41Tab. A8 Mean values of pillars’ variables, food security indicators, shocks and controls by sampling stratum . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

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ACKNOWLEDGEMENTS

This report has been prepared by the Resilience Analysis and Policies (RAP) team of the Food and Agriculture Organization of the United Nations’ (FAO’s) Agricultural Development Economics Division (ESA) with the collaboration of UNICEF and WFP. Special thanks go to Rebecca Pietrelli, Francesca Grazioli, Stefania Di Giuseppe, Marco d’Errico and Luca Russo for their contributions of technical information. Immaculate Atieno and Vu Hien from FAO Kenya contributed to data entry and data cleaning. Tomaso Lezzi and Giorgia Wizemann worked on the formatting and layout of the publication.

The team acknowledges Massimo Castiello and Benard Onzima from FAO Uganda for assisting them and liaising with the Joint Resilience Strategy partners.

Thanks are also extended to the Uganda Bureau of Statistics and in particular to Mr. Paul Opio for his active role and support during the entire data collection process.

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ACRONYMS

ABS Access to Basic Services

AC Adaptive Capacity

AST Assets

CAPI Computer Assisted Personal Interviewing

CSI Coping Strategy Index

DRR Disaster Risk Reduction

HDDS Household Dietary Diversity Score

JRS Joint Resilience Strategy

HH Household Head

LPM Linear Probability Model

MIMIC Multiple Indicators Multiple Causes

OPM Office of the Prime Minister

RAP Resilience Analysis and Policies (team)

RCI Resilience Capacity Index

RIMA Resilience Index Measurement and Analysis

SEM Structural Equation Model

SSN Social Safety Nets

ToC Theory of Change

TLU Tropical Livestock Units

UBoS Ugandan Bureau of Statistics

UNICEF United Nations Children’s Emergency Fund

WB World Bank

WFP Word Food Programme

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1 INTRODUCTIONThis section provides background information on the region of Karamoja and explains the aim of the report.

The region of Karamoja, located in the northeast of Uganda (see Figure 1), is an area of particular interest to many humanitarian organizations. Firstly, because food insecurity is a major challenge in the region. Half of the population in Karamoja is food insecure (UNICEF and WFP, 2016). Second, conflict both between communities (also known as clans) in Karamoja, and between communities in Karamoja and those in bordering countries1 (namely Kenya and Sudan), are rife (Finnström, 2008; Nannyonjo, 2005). Furthermore, insecurity associated with armed conflict has remained an issue in the region for decades (Saferworld, 2010). Additionally, a high level of climate variability undermines the capacity to utilize the region’s natural resources, as they are affected by droughts, floods and dry spells (USAID, 2017).

Figure 1. Uganda and Karamoja (by district) maps

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3

4

5

6 7

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1 Kaabong2 Abim3 Kotido4 Moroto5 Napak6 Nakapiripirit7 Amudat

Karamoja districtsKaramoja in UgandaUganda in Africa

1 Details on the different types of pastoralist conflicts in the Karamojong cluster can be found in USAID (2005).

Source:Own elaboration.

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The United Nations Children’s Emergency Fund (UNICEF), the Food and Agriculture Organization of the United Nations (FAO) and the World Food Programme (WFP) have been working in Karamoja for more than twenty years and together have developed a Joint Resilience Strategy (JRS) for the region. The overall goal of this JRS is to improve food security and nutrition status in the region during the period from 2016 to 2020.

FAO, WFP and UNICEF, in collaboration with the Ugandan Bureau of Statistics (UBoS) and the Office of the Prime Minister of Uganda (OPM), carried out data collection in November and December of 2016. This data was collected in order to be employed as a baseline survey, which will ultimately be used for assessing the impact of the JRS programming in the region.

This report presents the baseline analysis for Karamoja prior to the implementation of any JRS programming, using the above-mentioned data collected in 2016. The baseline analysis has been carried out in order to understand the initial situation faced by households in Karamoja before any JRS interventions had taken place; the results of this analysis will be used in future to create a comprehensive impact evaluation of the JRS programming, once panel data becomes available.

First and foremost, this report examines the differences in resilience capacity between households due to become beneficiaries and non-beneficiaries of the JRS programming, based on their geographical location. The indicators of interest – the Resilience Capacity Index (RCI) and so-called pillars of resilience – have been estimated using the FAO Resilience Index and Measurement Analysis (RIMA-II) methodology (see Annex I for a full explanation of this methodology).

The report is structured as follows: Section 2 presents the Theory of Change (ToC) of the JRS; Section 3 describes the impact evaluation strategy proposed for assessing the impact of the JRS; Section 4, the sampling strategy; and Section 5, the data collection process. Section 6 shows the tests carried out on the pre-treatment characteristics of beneficiary and non-beneficiary households. Finally, Section 7 concludes the findings of the report.

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2 THEORY OF CHANGEThis section focuses on the ToC of the JRS.

A frequently adopted definition of resilience is “the capacity that ensures adverse stressors and shocks do not have long-lasting adverse development consequences. It is the ability to absorb, adapt and transform in response to stress and shocks (climate, disease, economic, or conflict related).” (FAO, UNICEF and WFP, 2015). This definition supports the ToC of the above-mentioned JRS, which focuses on enhancing the resilience of the target households.

The overall goal of the JRS is to improve food security and nutrition status in the areas of the intervention during the time period of 2016-2020. The JRS aims to enhance resilience through:

1. strengthening productive sectors to increase household income and strengthen food security by diversifying livelihood strategies, intensifying production and productivity at the household level, and improving access to markets;

2. improving basic social services to strengthen vulnerable households’ human capital. This is done by creating systems capable of assessing communities and capturing the information needed to enhance the demand for and access to health and social care practices and capacity building opportunities;

3. establishing predictable safety nets to address the most vulnerable people’s basic needs. This is done through the regular and sustainable transfer of food or cash for extremely vulnerable or seasonally at risk populations; and

4. strengthening disaster risk management (DRM) support, including early warning systems (EWS), contingency planning, risk financing, and early responses to protect vulnerable households and ass ets, and overall investment in development.

The ToC (see Figure 2) identifies the four above-mentioned areas as key channels for enhancing absorptive, adaptive and transformative capacities and, therefore, resilience capacity. This will ultimately allow households and communities to have an improved response to shocks and stressors in order to limit the negative effects on food security and nutrition.

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Figure 2. ToC of the JRS

FOOD SECURITY AND NUTRITION

IMPROVED RESPONSE TO SHOCKS

LOCAL CONTEXT FACTORS:ECONOMIC, SOCIAL, POLITICAL, ENVIRONMENTAL (CLIMATE CHANGE),

HISTORIC, DEMOGRAPHIC, RELIGIOUS, POLICY and CONFLICT

ADAPTIVE CAPACITYABSORPTIVE

CAPACITYTRANSFORMATIVE

CAPACITYADAPTIVE CAPACITY

DISASTER RISK REDUCTION

CASH SAVINGS

HOUSEHOLD PERCEIVED

ABILITY TO RECOVER FROM SHOCKS

SOCIAL CAPITAL

ACCESS TOINFORMAL COMMUNITY

SAFETY NETS

ASSET OWNERSHIP

HAZARD INSURANCE

GOVERNANCE

EMPOWERMENT OFWOMEN, CHILDREN

AND ELDERLY

FORMAL SAFETY NETSAVAILABILITY

SOCIAL CAPITAL

ACCESS TO:INFRASTRUCTURES

BASIC SERVICESAGRICULTURAL

SERVICESNATURAL RESOURCES

MARKETS

HOUSEHOLDASPIRATIONS CONFIDENCE

TO ADAPT

ACCESS TO INFORMATION

HUMAN CAPITAL

SOCIAL CAPITAL

DEGREE OFLIVELIHOOD

DIVERSIFICATION

For improving the absorptive capacity of households, interventions should focus on the ability of households, as well as the broader community and socio-economic systems, to manage shocks and stressors in the short term through cash savings, informal safety nets, the disposal of liquid assets, disaster risk reduction strategies, hazard insurance and social capital (FAO, UNICEF and WFP, 2015).

h An expansion of networks, and hence of social safety nets, offers a household a broader array of possible avenues for accessing assistance when faced with extreme challenges.

For improving adaptive capacity, interventions should enable people and the socio-economic system where they live to proactively adapt to changing conditions through. This can be done, for example, via better access to information, the ability to accumulate assets (to be sold in the event of a shock and thus smooth consumption) and improved access to financial services. Additional interventions should be focused in livelihood diversification (spanning livelihoods with varying risk profiles), investment in human capital for better access to skills, and improved nutrition and health status (FAO, UNICEF and WFP, 2015).

h Strengthening a household’s decision-making capability leads to choices that can improve the overall well-being of the household, even in a context of limited resources.

h An increase in assets leads to better well-being. Productive assets are essential in shaping a household’s livelihood; they allow a household to produce consumable or tradable goods. A household with a variety of assets is well placed to survive shocks and stressors.

Source:FAO, UNICEF, WFP, 2015

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For transformative capacity, investments should be geared towards improved governance, as well as access to markets, basic services, agricultural services, natural resources and infrastructure, and empowering women, children, the elderly and the disabled (FAO, UNICEF and WFP, 2015).

h Improved access to basic services is linked to an increase in household production capacity, and subsequently an increase in income sources. As an example, in the case of farmer households, having access to market institutions offers an opportunity to sell their products quickly without being faced with losses due to damaged or perishable goods. Meanwhile, access to hospitals and/or health facilities is linked to the improved health of individuals and the well-being of households.

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3 IMPACT EVALUATION STRATEGYThis section explains the impact evaluation strategy proposed for analysing the effect of the JRS on beneficiary households after the implementation of the JRS programming, and once panel data has become available.

The JRS programming, encompassing a number of projects and initiatives to be delivered from 2016 to 2020, has already commenced in Karamoja. While this present report deals only with carrying out a baseline analysis of the resilience and food security scenario in Karamoja prior to the implementation of JRS programming, in order to carry out the baseline analysis effectively. The baseline analysis is crucial to plan ahead for the future impact evaluation strategy that will be carried out down the track in order to understand the impact of the JRS programming. The impact evaluation will be carried out once panel data on Karamoja becomes available, and this baseline analysis will be used to complete that impact evaluation.

Thus, the impact evaluation strategy has already been initially designed for the purposes of carrying out this baseline analysis effectively. When carried out, the impact evaluation strategy must answer the question: has the JRS improved the resilience capacity of beneficiary households? The beneficiaries are households located in the selected areas2 of Moroto and Napak – which are districts within the region of Karamoja – which are currently participating in projects implemented by the JRS. In further detail, the impact evaluation strategy will aim to demonstrate: (i) whether the JRS will have increased the resilience capacity of beneficiary households compared to non-beneficiary households, and (ii) if so, through which mechanisms the improved resilience capacity will have taken place.

For assessing the effect of the JRS, at least two rounds of data will be needed for the analysis, collected approximately 1.5 to two years apart from one another. This baseline analysis offers the first round of data; for the next round of data collection, the same households will be interviewed in order to create a panel dataset. Any further rounds of data, should they be collected in future, could be used to further enrich the impact evaluation.

2 The areas selected for the JRS programming are in fact known as ‘parishes’, which are the lowest regional administrative level used in Uganda.

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The indicators of interest are:

h the RCI and the resilience pillars, which are estimated using the RIMA-II methodology;3

h the variables that are part of the RCI estimation process; and

h other indicators, which the three agencies that are part of the JRS will deem relevant to the programmes associated with the JRS.

The main challenges of the quantitative analysis for assessing the impact of the JRS are (1) the non-random selection of beneficiary households, (2) the presence of a potential spillover between beneficiary and non-beneficiary groups, and (3) the nature of the treatment group of interest – each of these points are explained in further detail below.

For the pending impact evaluation, a tailor-made statistical methodology will be employed for taking into account these challenges, in order to minimize the possibility of any erroneous effects in the estimation of the effect of the JRS on the resilience capacity of beneficiary households. For the purposes of this baseline analysis, these challenges have also been taken into consideration when designing the approach to assessing this initial round of data.

1. Non-random selection of beneficiary households.

The JRS is a multi-year and multi-agency programme. Therefore, given the multifaceted nature of the interventions, random selection of beneficiaries was not possible. Additionally, the different types of JRS interventions may have different types of beneficiaries according to the projects’ design, especially in the case of household with short-term difficulties versus household with long-term difficulties. For example, one intervention may target households with a long-term capacity to emerge from poverty while another may target households with chronic persistency in poverty.

If a random selection of beneficiaries were to be used in the analysis, this would be able to control for self-selection bias (Duflo, Glennerster and Kremer, 2007); namely, this would control for the differences between beneficiary and non-beneficiary households in observable and unobservable characteristics, which may confound the assessment of the effect of the JRS on resilience capacity. Therefore, in this case utilising a non-random selection (that distinguishes between beneficiary and non-beneficiary households, as explained above) also requires the use of quasi-experimental statistical techniques, such as the Difference-in-Difference (DiD) method or Propensity Score Matching (PSM), or a combination of the two techniques.

The DiD methodology (Card and Krueger, 1994) estimates the effect of the treatment – in this case, the JRS – by comparing the changes in outcomes over time between a population that has received the interventions (the treatment group) and a population that has not (the control group). The DiD estimator takes into account time-invariant differences between the treatment and control groups that may confound the estimation of the treatment effect. The key assumption for identifying the effect of the treatment through the DiD estimator is the Parallel Trend Assumption, which requires that the difference between the treatment and the control groups is constant over time (Angrist and Pischke, 2008). The PSM, according to Rosenbaum and Rubin (1983), relies on matching estimators that take into account observable differences between the treatment and control groups (Angrist and Pischke, 2008).4 As the imminent impact evaluation will use a panel data set, the DiD method (or the combination of the DiD method and PSM) will be employed within that analysis.

3 For further information on the RIMA-II methodology, see FAO (2016) listed in the references list, and Annex I.4 The key assumption in the PSM method is Conditional Independence Assumption. This means that, conditioning on

observable characteristics, participation in the treatment is independent of the potential outcomes of that treatment.

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In this report, the differences in observable characteristics at the baseline between the treatment and the control groups are tested in Section 6. The balance test is a key step in order to design the appropriate statistical approach, and thus has been used in this analysis.

2. Spillover between beneficiary and non-beneficiary households.

Spillovers may arise when non-beneficiary households are inadvertently receiving some positive effects of a particular programming, even though those non-beneficiary households are not officially part of the given programme. One type of spillover is so-called externalities, as described in Miguel and Kremer (2004). For example, the implementation of a vaccine can affect a broader population than just the direct recipients of the vaccine, because it can decrease overall disease transmission. Additionally, again using the example of a vaccination, the non-treated population may be indirectly positively affected by the treatment through social and economic interactions with the beneficiary population (Angelucci and De Giorgi, 2009). In terms of assessing the impact of the JRS, it is this type of scenario (where there may be such externalities or interactions) that can cause spillover effects, which reduces the possibility of detecting the exact effect of the JRS on household resilience capacity.

In this analysis, the sampling strategy was set in order to take into account this issue. In fact, the control group is comprised of households located far away from the areas where the JRS interventions took place. Therefore, any spillover effects are hypothesized as absent from the treatment and control groups. In addition, a second control group, composed of non-beneficiary households located in the same districts as the treatment groups, was created in order to study the presence of any potential spillover effects. Details on the sampling strategy are provided in Section 5.

Thus, the quantitative analysis carried out in this report estimates the effect of the treatment by comparing the treatment group with a ‘pure’ control group (households located in a district far away from the location of the treated households) and with another control group for which a potential spillover may be present; the econometric models employed will thus be: (i) treatment group versus control group without any spillover effect, (ii) treatment versus control group with potential spillover effects.

3. Nature of the treatment of interest.

The treatment of interest, the JRS, is a multi-year and multi-agency programme, including specific projects implemented over the time period from 2016 to 2020. Therefore, the effect of each specific project or programme cannot be disaggregated; only the joint effect of the JRS as a whole will be analysed by the forthcoming impact evaluation.

Nevertheless, an outline of all the projects implemented in Karamoja at the parish level according to the year of implementation could be helpful – this could be used to take into account any potential differences in the scope and types of interventions offered in the different areas where the treatment group is located, as one parish may be the recipient of more and/or different projects than another. Additionally, at the project level, if information were to be collected about the household regarding which projects they are eligible for and have been the recipient of, then this could also help to further understand the effects of the JRS. For instance, if the household has a female head, they would have been eligible for specific projects related to that criteria. Moreover, in the follow-up survey specific questions on the participation of the household in the JRS programming will be included to clarify the number of JRS activities implemented.

However, the multi-agency, multi-year, multi-project nature of the JRS programming cannot be specifically accounted for in this baseline analysis; this will simply need to be considered within the impact evaluation, as described above.

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4 DATA COLLECTION STRATEGYThis section explains the baseline household survey data collection strategy. It provides details on the main data collection instruments, such as the training for the enumerators and the questionnaire that was used to conduct the survey. The baseline household survey is the first round of data collected in Karamoja during later 2016, which is used in this baseline analysis.

The baseline household survey was conducted in the Karamoja region from 13 November to 10 December 2016.

Training for the enumerators to carry out the survey took place in the Moroto district in Karamoja from 7 to 11 November 2016, under the guidance of FAO, UBoS, WFP, UNICEF, the FAO’s Resilience Analysis Units in Nairobi and in Kampala, and the OPM. 44 enumerators attended the training, which involved:

h familiarization with the survey’s modules and structure;

h contextualization of the questionnaire in order to adapt the context-specific modules when conducting the survey, such as that the module for calculating the Coping Strategy Index (CSI) reviewed through Focus Group Discussions (FGDs) (Maxwell and Caldwell, 2008). The CSI is a weighted sum of the number of days out of the past week that a household adopted different strategies to cope with food shortages. The list of strategies and their relative weights5 are drawn from a FGD carried out during the enumerator training;

h simulations of interviews using both paper and digital tablets; and

h preparation, implementation and revision of the pilot of the survey, which took place on November 11 in Moroto.

5 Details are provided in Table A1.

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The household survey6 was developed by FAO in collaboration with UBoS, UNICEF and WFP, using a questionnaire7 comprised of thematic sections. Specifically, this investigates detailed information on household characteristics, food and non-food consumption, shocks, perceived resilience capacity, coping strategies, and so on. Additionally, data regarding labour, education and use of time were collected from individuals within the households.

The data collection for the household survey was carried out using Computer-Assisted Personal Interviewing (CAPI) methods, using digital tablets for conducting the interviews. This presents many advantages compared to the traditional paper questionnaire: they reduce the time of the interview, limit errors during both the interview and data entry phases, and allow for the collection of Geographic Information System (GIS) information at the household level.

The main limitation of the data from this household survey is its cross-sectional dimension – that is, that the interviews relate to one moment in time – but this will be addressed by the fact that the second round of data will be collected to create the above-mentioned panel dataset. A second limitation of the data is that there was a lack of information collected on the quantities of food items consumed in the consumption module of the household survey. Additionally, child malnutrition indicators, based on anthropometric measures, were unfortunately not collected in the household survey.

6 In addition to the household survey employed in this analysis, a community survey, collected in 24 communities in all the seven districts of the Karamoja region, has been also implemented. The survey includes both qualitative and quantitative components, covering mainly service and infrastructure availability, shocks and coping strategies, international assistance and enabling institutional environment. The main objective of the community survey is to understand (i) how the different livelihoods of the region (pastoralist, agro-pastoralist and farmer) inter-relate to each other across the region’s climatic zones, and (ii) cross-border dynamics, mostly between Uganda and Kenya.

7 The definition of ‘household’, as defined by the FAO RAP team (which carried out both the household survey and this analysis) is: “a household is formed by all the people living in the same hut or home, related or not by blood lines (family) and sharing food, food expenses, income and other household assets for at least 6 of the 12 months preceding the interview. Therefore, the membership of the household is defined on the basis of the usual place of residence”.

13


5 SAMPLING STRATEGY AT THE BASELINEThis section explains the sampling strategy adopted for the baseline data collection.

The sampling selection for the baseline data collection is based on a random sampling of households, carried out in each of the seven districts in the Karamoja region. The sample was drawn in two stages. In the first stage, the Primary Sampling Units (PSU) – the parishes – were selected both from the areas where the JRS will be implemented (i.e. the treatment group) and will not be implemented (i.e. the control group). In the second stage, the Secondary Sampling Units (SSU), the households, were randomly selected from the treatment and control groups.

The sample size for each district was calculated by taking into account the:

h level of statistical power (the probability of detecting the effect) of the pending impact evaluation, set at 90 percent;

h level of significance of the pending impact evaluation, set at 95 percent, to reduce the probability of a type I error;

h expected impact of the JRS programming in Karamoja; an impact of at least a 10 percent increase in the RCI was assumed, while the average standard deviation for the RCI is estimated at 0.25, based on the RIMA-II analysis previously carried out for Dolow in Somalia (FAO, UNICEF and WFP, 2016);

h non-response rate from the households, which meant the sample size was slightly increased to account for this;

h household composition;

h possibility of attrition (to obtain a proper panel data set and control for attrition, the final sample is increased by 10 percent);

h control group selection (a 10 percent increase in the sample size was added, in order to allow for perfect matching – by employing matching estimators – between the treatment and control groups); and

h impact evaluation design. A control group of households located in a district (Kaabong) that is far away from the Napak and Moroto districts, where the JRS programming has been implemented, is included in this analysis as a sample stratum – this group will be utilised as counterfactual in the impact evaluation analysis.

KARAMOJA 2016

14

By following the approach for sample size calculation outlined in Magnani (1999), the final figure obtained for this analysis is 340 households for each of the seven districts. Bearing this figure in mind, the number of surveyed households in the treatment group, pure control group and indirect spillover group has been oversampled in order to avoid any possible errors (e.g. attrition) and to ensure a more robust analysis. The total sample from the survey is composed of 2 380 households.

Five sampling strata have been set up in order to ultimately capture both the direct effect of the JRS programming on beneficiary households, and any potential indirect spillover effects, once the impact evaluation is carried out:

1. treatment group; the households that are targeted by and have received JRS interventions, across 12 parishes in the Moroto and Napak districts (a total of 661 households);

2. direct spillover households group; the households located in the remaining parishes in the Moroto and Napak districts, which are not involved in the JRS programming. This stratum allows for measuring any potential direct spillover effects of the JRS interventions (in this stratum, there are 313 households);

3. indirect spillover households group; the households located in the two districts where the JRS programming is not operating (Kotido and Nakpirpirit). However, there are many other non-JRS aid projects ongoing in these two districts, ranging from one to six projects in each parish. Therefore, it can be assumed that households in these districts may be benefitting from indirect positive effects of, for example, a water well rehabilitation programme in a neighbouring parish (in this stratum, there are 599 households);

4. different ethnicity group; the households located in two districts (Abim and Amuday) that are populated with non-Karamojong people (in this stratum, there are 404 households);8 and

5. pure control group; the households located in the Kaabong district. These households are occupied by people of the same ethnic cluster (the Karamojong people) and socioeconomic conditions (who pursue mostly pastoralism) as the treatment group. However, the JRS programming has not and will not be implemented in this district. In some parts of this district, there are one to two other UN projects present. This district is located very far from the area where the JRS programming is implemented, so it is very unlikely that households in this district would indirectly benefit from that programming (in this stratum, there are 403 households).

Figure 3 offers a geographical representation of the sampling strata across Karamoja, while Table 1 shows the correspondence between the sampling strata and the districts in which the households are located.

8 The Karamojong is a generic term for the Nilotic people of the Karamoja region. The Karamojong cluster includes Dodoth, Jie and Karimojong people (Gradé, Tabuti and Van Damme, 2009). The three groups speak closely related languages and dialects. On the other hand, the ethnic groups living in Abim and Amudat districts are mainly Labwor and Pokot, not belonging to the Karamojong cluster (OCHA, 2006).

15


Figure 3. Sampling strata map

LegendInternational boundary

Karamoja districts

Subcounty boundary

Target

Direct spillover

Indirect spillover

Different ethnicity

Pure control

Source:FAO Uganda, January 2016

Table 1. Number of interviewed households by district and sampling stratum

StrataDistrict N. of

householdsPercentage

of totalAbim Amudat Kaabong Kotido Moroto Nakapiripirit Napak

Treatment group - - - - 327 9 - 334 10 661 27.77

Direct spillover

group- - - - 167 - 146 313 13.15

Indirect spillover

group- - - 297 - 302 - 599 25.17

Different ethnicity

group204 200 - - - - - 404 16.97

Pure control group - - 403 - - - - 403 16.93

Total 204 200 403 297 494 302 480 2 380 100.00

Sample weights have been calculated as probability weights proportional to the subcounty size. The weights are reported in Table A3 in Annex II.

9 This includes households located in the following parishes: Lia; Musas; Nagunget; Naitakwe; Loyaraboth and Tapac.10 This includes households located in the following parishes: Apeitolim; Lorikitae; Lokodumu; Lopeei; Naguleangolol

and Ngoleiriet (or in the following subcounties: Lokopo, Lopeei, and Ngoleriet).

KARAMOJA 2016

16

In summary, the total sample of households in the treatment group is composed of 661 households, while the pure control group consists of 403 households. To control for potential spillover effects, two comparison groups have been designed; one for considering potential direct spillover effects (with a total of 313 households) and one for considering potential indirect spillover effects (with a total of 599 households). Thus, these two spillover effect groups mean that a total of 912 households will be compared against the treatment group. Finally, a third comparison group has been designed – the different ethnicity group, consisting of 404 households, which is made up of households whose members belong to ethnic groups other than the Karamojong people. This allows to analysis to be representative at regional level and to test for the presence of potential (but unexpected) spillover between different ethnic groups.

17


6 BALANCE TESTThis section focuses the balance test carried out on the RCI, resilience pillars and resilience pillar variables between the treatment group and the other four sampling strata.

This section shows the main findings of the balance test. The aim is to test whether at the baseline – that is, before the implementation of the JRS programming – households that will ultimately become the beneficiary of the JRS programming differ from households that will not be involved in the programming. The outcomes of interest are (i) the Resilience Capacity Index (RCI), (ii) the resilience pillars, and (iii) the variables that determine the resilience pillars.

Thus, the balance test investigates whether, prior to the implementation of JRS programming, the treatment group households differ from the control group households in terms of their RCI, resilience pillars and resilience pillar variables.

Other comparisons made in this section involve the treatment group compared to the direct spillover group, indirect spillover group and different ethnicity group, respectively. Therefore, the comparisons being made here are four in total:

1. the treatment group versus the direct spillover group; 2. the treatment group versus the indirect spillover group; 3. the treatment group versus the different ethnicity group; and4. the treatment group versus the pure control group.

A t-test has been employed to assess whether the differences in all the adopted indicators (the RCI, as well as the resilience pillars11 of Access to Basic Services (ABS), Assets (AST), Social Safety Nets (SSN) and Adaptive Capacity (AC)) and all the resilience pillar variables are statistically significant across the four comparison groups.

Table 2 shows the mean values of the RCI for each of the sampling strata. Moreover, differences in RCI between each of the sampling strata are tested against the four comparison groups by employing a t-test. The same format applies to the tables from Table 3 to Table 6, wherein each of the resilience pillars (ABS, AST, SSN and AC) are compared across each of the sampling strata.

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

KARAMOJA 2016

18

In Table 7, each of the variables that make up the resilience pillars are explored across each of the sampling strata. For the resilience pillars and variables, the t-test was employed for assessing the statistical significance of the difference in mean values (for both the resilience pillars and the resilience pillar variables) by sampling strata.

In terms of RCI (see Table 2), the treatment group households have a higher RCI than those in the pure control group, and a lower RCI compared to those in the direct spillover and different ethnicity groups. Any statistically significant difference in RCI is detected between the treatment group and indirect spillover group.

Table 2. Mean RCI by sampling stratum

Treatment Direct spillover

Indirect spillover

Different ethnicity

Pure control

Mean RCI 44.515(0.633)

48.003(0.949)

44.066(0.716)

48.345(0.898)

40.107(0.740)

Observations 661 313 599 404 403

Treatment - Direct

spillover

Treatment - Indirect spillover

Treatment - Different ethnicity

Treatment - Pure control

Difference in mean SSN

figures -3.487***(1.129)

0.449(0.953)

-3.829***(1.073)

4.408***(0.996)

Observations 974 1 260 1 065 1 064

Standard errors in parentheses. P-value of t-test: *** p<0.01, ** p<0.05, * p<0.1

The treatment group households have lower figures for ABS, AST and AC than households in the pure control group (shown in Table 3, Table 4 and Table 6), but have higher food security as measured by the Household Dietary Diversity Score (HDDS)12 (see Table 7). As shown in Table 7, the main drivers of the difference in the figures for the ABS pillar are the indicators of proximity to basic services (school, hospital and market), which show higher values for the pure control group than for the treatment group. On the contrary, the treatment group is better off than the pure control group in terms of access to improved water sources and water availability. Looking at the AST pillar, the difference between the treatment and pure control groups is mainly explained by the lower values for non-productive assets, such as wealth index and house value, reported for households in the treatment group. Despite the fact that a statistically significant difference in the SSN pillar is not detected among target and control households (Table 5), households in the treatment group do receive a (statistically significant) greater amount of formal transfers than households in the pure control group. The main drivers of the lower figures for the AC pillar for the treatment group are lower education, a lower number of income-generating activities, a lower number of cultivated crops and a higher CSI than for the pure control group.13

Treatment group households have a lower RCI than households in the direct spillover group. This is driven by their lower figure for ABS – mainly, lower access to improved sanitation, and closeness to school and markets – as well as lower food security (measured via the HDDS).

12 The HDDS is the number of the food groups consumed by the household during the previous seven days. The considered groups of food are cereals, tubers, vegetables, fruits, meat, egg, fish, pulses, milk, oil, sugar, miscellaneous (Swindale and Bilinsky, 2006).

13 Table A4 in Annex III shows the results of the Linear Probability Model (LPM) and Probit model of being a treatment group household versus a pure control group household while Figure A1 the distribution of the estimated propensity score.

19


However, the treatment group has a higher figure for both SSN (mainly caused by a higher level of current credit, contracted in the last 12 months) and AC, driven by a higher number of cultivated crops and larger share of active household members, compared to households in the direct spillover group. Despite the fact that a statistically significant difference is not detected in for AST as a whole between the treatment and direct spillover groups, the treatment households have higher values for productive assets, such as Tropical Livestock Units (TLU) and total land for cropping.14

Table 3. Mean ABS by sampling stratum


Indirect spillover

Different ethnicity

Pure control

Mean ABS -0.029(0.016)

0.096 (0.047)

0.032 (0.032)

-0.246 (0.041)

0.171 (0.072)

Observations 661 313 599 404 403

Treatment - Direct

spillover





figures -0.125***(0.040)

-0.061* (0.035)

0.217***(0.038)

-0.200***(0.060)

Observations 974 1 260 1 065 1 064


Table 4. Mean AST by sampling stratum


Indirect spillover

Different ethnicity

Pure control

Mean AST -0.256 (0.029)

-0.317 (0.048)

-0.165 (0.036)

0.583 (0.059)

0.328 (0.058)

Observations 661 313 599 404 403

Treatment - Direct

spillover





figures 0.060

(0.054)-0.091**(0.046)

-0.840***(0.059)

-0.585***(0.059)

Observations 974 1 260 1 065 1 064


The RCI is not statistically different between the treatment and indirect spillover households. Minor differences between the two groups are found in the resilience pillar figures, namely higher ABS and lower AST for the treatment group compared to the indirect spillover group. Treatment households have lower ABS compared to indirect spillover households, mainly explained by greater distances to schools, and to agricultural and livestock markets. Looking at AST, treatment households have a higher agricultural asset index and more total land for cropping, but a lower reported house value.15

14 Table A5 in Annex III shows the results of the Linear Probability Model (LPM) and Probit model of being a treatment group household versus a direct spillover group household while Figure A2 the distribution of the estimated propensity score.

15 Table A6 in Annex III shows the results of the Linear Probability Model (LPM) and Probit model of being a treatment group household versus a indirect spillover group household while Figure A3 the distribution of the estimated propensity score.

KARAMOJA 2016

20

Table 5. Mean SSN by sampling stratum


Indirect spillover

Different ethnicity

Pure control

Mean SSN -0.049(0.026)

-0.168(0.025)

0.061(0.067)

0.067(0.068)

0.051(0.075)

Observations 661 313 599 404 403

Treatment Direct

spillover

Treatment Indirect

spillover

Treatment Different ethnicity

Treatment Pure

control


figures 0.119***

(0.042)-0.110(0.069)

-0.117**(0.063)

-0.100(0.068)

Observations 974 1 260 1 065 1 064


Households in the different ethnicity group have a higher RCI than treatment group households. This is mainly driven by their higher level of food security (based on measurements of food consumption), and higher figures for AST, AC and SSN for different ethnicity households. On the contrary, treatment group households have higher ABS. In terms of AST, treatment group households have lower values for their wealth index, TLU and house value. The main drivers of the difference in AC between the two groups are lower education, a higher CSI and lower number of income-generating activities for treatment households, compared to households in the different ethnicity group. Despite treatment group households having a lower SSN than households from the different ethnicity group, an interesting difference is detected in the composition of the SSN pillar: treatment households receive a lower amount of credit per capita, but a higher amount of formal transfers per capita.16

Table 6. Mean AC by sampling stratum


Indirect spillover

Different ethnicity

Pure control

Mean AC -0.172(0.040)

-0.301(0.065)

-0.193(0.043)

0.357(0.072)

0.446(0.070)

Observations 661 313 599 404 403

Treatment - Direct

spillover





figures 0.129*

(0.073)0.020

(0.059)-0.529***(0.076)

-0.619***(0.075)

Observations 974 1 260 1 065 1 064


16 Table A7 in Annex III shows the results of the Linear Probability Model (LPMs) and Probit model of being a treatment group household versus a different ethnicity group household while Figure A4 the distribution of the estimated propensity score.

21


Tabl

e 7.

Res

ilien

ce v

aria

bles

by

sam

plin

g st

ratu

m

Targ

etPu

re co

ntro

l

(1)

Diffe

renc

e Ta

rget

-

Pure

cont

rol

Dire

ct

spill

over

(2)

Diffe

renc

e Ta

rget

-

Dire

ct

spill

over

Indi

rect

sp

illov

er

(3)

Diffe

renc

e Ta

rget

-

Indi

rect

sp

illov

er

Diffe

rent

et

hnic

ity

(4)

Diffe

renc

e Ta

rget

-

Diffe

rent

et

hnic

ity

ABS

Impr

oved

san

itatio

n0.

055

0.44

9-0

.395

***

0.09

6-0

.041

*0.

127

-0.0

72**

*0.

198

-0.1

44**

*(0

.227

)(0

.498

)(0

.026

)(0

.295

)(0

.019

)(0

.333

)(0

.016

)(0

.399

)(0

.022

)

Impr

oved

wat

er0.

980

0.87

10.

109*

**0.

965

0.01

550.

937

0.04

4***

0.88

10.

099*

**(0

.139

)(0

.336

)(0

.018

)(0

.184

)(0

.012

)(0

.244

)(0

.011

)(0

.324

)(0

.017

)

Wat

er a

vaila

bilit

y11

.590

11.3

600.

237*

11.3

800.

214*

11.5

300.

065

10.8

400.

758*

**(1

.073

)(1

.718

)(0

.095

)(1

.646

)(0

.102

)(1

.240

)(0

.066

)(1

.766

)(0

.097

)

Clos

enes

s to

sch

ool

0.06

50.

135

-0.0

70**

*0.

140

-0.0

75**

*0.

104

-0.0

39**

*0.

100

-0.0

35**

*(0

.102

)(0

.195

)(0

.011

)(0

.263

)(0

.015

)(0

.158

)(0

.008

)(0

.119

)(0

.007

)

Clos

enes

s to

hos

pita

l / h

ealth

faci

lity

0.06

10.

121

-0.0

60*

0.05

40.

008

0.05

30.

009

0.06

8-0

.007

(0.2

29)

(0.5

37)

(0.0

28)

(0.1

02)

(0.0

11)

(0.1

39)

(0.0

11)

(0.1

13)

(0.0

11)

Clos

enes

s to

live

stoc

k m

arke

t0.

021

0.09

7-0

.075

***

0.05

6-0

.035

***

0.04

4-0

.022

**0.

027

-0.0

05(0

.050

)(0

.292

)(0

.015

)(0

.164

)(0

.009

)(0

.157

)(0

.007

)(0

.160

)(0

.008

)

Clos

enes

s to

agr

icul

tura

l mar

ket

0.03

40.

104

-0.0

70**

0.05

9-0

.025

**0.

074

-0.0

40*

0.04

0-0

.005

(0.0

90)

(0.5

36)

(0.0

27)

(0.1

52)

(0.0

09)

(0.4

35)

(0.0

18)

(0.0

70)

(0.0

05)

AST

Wea

lth in

dex

0.11

30.

218

-0.1

05**

*0.

115

-0.0

030.

115

-0.0

020.

264

-0.1

51**

*(0

.160

)(0

.233

)(0

.013

)(0

.181

)(0

.012

)(0

.157

)(0

.009

)(0

.233

)(0

.013

)

Agri

cultu

ral a

sset

inde

x0.

255

0.26

4-0

.010

0.25

10.

004

0.23

30.

022*

**0.

229

0.02

6***

(0.1

27)

(0.1

13)

(0.0

08)

(0.1

32)

(0.0

09)

(0.1

02)

(0.0

06)

(0.0

95)

(0.0

07)

TLU

0.67

10.

754

-0.0

830.

459

0.21

2**

0.66

30.

008

1.44

8-0

.777

***

(1.4

20)

(1.9

62)

(0.1

12)

(1.0

76)

(0.0

82)

(1.2

87)

(0.0

76)

(2.8

06)

(0.1

50)

Land

for

crop

ping

3.70

63.

721

-0.0

153.

195

0.51

1***

2.27

51.

431*

**2.

250

1.45

6***

(2.3

76)

(2.4

60)

(0.1

53)

(2.1

76)

(0.1

54)

(1.5

65)

(0.1

12)

(1.6

33)

(0.1

23)

Hou

se v

alue

47.7

4010

8.80

0-6

1.09

0***

50.8

50-3

.114

104.

900

-57.

200*

**14

3.10

0-9

5.31

0***

(75.

870)

(174

.900

)(9

.200

)(1

04.5

00)

(6.6

01)

(149

.900

)(6

.799

)(1

89.3

00)

(9.8

69)

KARAMOJA 2016

22

Tabl

e 7.

Res

ilien

ce v

aria

bles

by

sam

plin

g st

ratu

m (c

ont.)

Targ

etPu

re co

ntro

l

(1)

Diffe

renc

e Ta

rget

-

Pure

cont

rol

Dire

ct

spill

over

(2)

Diffe

renc

e Ta

rget

-

Dire

ct

spill

over

Indi

rect

sp

illov

er

(3)

Diffe

renc

e Ta

rget

-

Indi

rect

sp

illov

er

Diffe

rent

et

hnic

ity

(4)

Diffe

renc

e Ta

rget

-

Diffe

rent

et

hnic

ity

SSN

Cred

it pc

1.04

21.

608

-0.5

660.

416

0.62

7***

1.24

0-0

.198

2.02

3-0

.981

**(2

.971

)(7

.698

)(0

.401

)(1

.709

)(0

.151

)(4

.463

)(0

.216

)(6

.947

)(0

.364

)

Past

cre

dit p

c0.

311

0.67

2-0

.361

0.20

80.

102

0.90

8-0

.597

*0.

476

-0.1

65(1

.768

)(3

.830

)(0

.203

)(1

.260

)(0

.099

)(6

.186

)(0

.262

)(3

.216

)(0

.174

)

Form

al tr

ansf

ers

pc4.

657

1.45

63.

201*

**3.

835

0.82

22.

551

2.10

6***

1.86

12.

796*

**(1

1.96

0)(6

.751

)(0

.574

)(9

.101

)(0

.694

)(9

.562

)(0

.608

)(9

.808

)(0

.674

)

Info

rmal

tran

sfer

s pc

1.07

91.

597

-0.5

171.

032

0.04

70.

995

0.08

51.

287

-0.2

08(5

.299

)(6

.444

)(0

.381

)(5

.463

)(0

.371

)(8

.672

)(0

.410

)(7

.924

)(0

.445

)

AC Aver

age

educ

atio

n1.

640

2.52

1-0

.881

***

1.77

2-0

.132

1.80

7-0

.167

2.56

1-0

.921

***

(2.5

40)

(2.8

82)

(0.1

74)

(2.7

03)

(0.1

82)

(2.8

12)

(0.1

52)

(3.2

91)

(0.1

91)

Shar

e of

act

ive

hous

ehol

d m

embe

rs0.

482

0.47

60.

005

0.44

60.

036*

*0.

460

0.02

10.

487

-0.0

05(0

.206

)(0

.192

)(0

.013

)(0

.195

)(0

.014

)(0

.190

)(0

.011

)(0

.191

)(0

.012

)

CSI (

inve

rse)

0.06

70.

147

-0.0

81**

*0.

069

-0.0

020.

062

0.00

50.

133

-0.0

67**

*(0

.175

)(0

.285

)(0

.016

)(0

.178

)(0

.012

)(0

.158

)(0

.009

)(0

.242

)(0

.014

)

N. i

ncom

e so

urce

s2.

256

2.45

9-0

.203

**2.

176

0.08

02.

394

-0.1

38*

2.47

3-0

.217

**(1

.114

)(1

.067

)(0

.069

)(1

.162

)(0

.079

)(1

.089

)(0

.062

)(1

.034

)(0

.067

)

N. c

ultiv

ated

cro

ps2.

477

2.93

8-0

.461

***

2.20

80.

269*

**2.

147

0.33

0***

2.66

1-0

.184

(1.1

46)

(1.7

40)

(0.0

98)

(1.1

46)

(0.0

79)

(1.2

80)

(0.0

69)

(2.0

26)

(0.1

10)

FS Food

con

sum

ptio

n pc

58.2

5054

.020

4.22

960

.100

-1.8

5457

.100

1.14

682

.170

-23.

920*

**(4

0.07

0)(3

2.16

0)(2

.235

)(3

9.67

0)(2

.731

)(4

3.26

0)(2

.357

)(4

7.39

0)(2

.826

)

HD

DS

6.03

55.

231

0.80

4***

6.52

7-0

.492

***

5.96

50.

070

6.10

6-0

.072

(1.9

12)

(1.6

31)

(0.1

10)

(1.9

25)

(0.1

32)

(2.0

59)

(0.1

12)

(2.0

96)

(0.1

28)

Stan

dard

err

ors

in p

aren

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es. P

-val

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est:

***

p<0.

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* p<

0.05

, * p

<0.1

23


7 CONCLUSIONSThis section concludes with future steps for the upcoming impact evaluation of the JRS programming.

The overall goal of the JRS, implemented by UNICEF, FAO and WFP, is to improve food security and nutrition status in the areas where interventions are set to take place during the time period 2016-2020. FAO, WFP and UNICEF, in collaboration with the UBoS and the OPM, carried out data collection in Karamoja in November and December 2016 to be primarily employed as baseline survey for assessing the future impact of the JRS programming after its implementation.

The aim of this report is to establish the first step in a longer-term impact evaluation. The report provides the results of the baseline analysis. It mainly focuses on differences in the RCI, resilience pillars and observed resilience pillar variables between households set to become beneficiary and non-beneficiary households of the JRS programming, prior to the implementation of that programming. The RCI, as well as the resilience pillars, have been estimated using the RIMA-II methodology.

The forthcoming impact evaluation of the JRS programming will be conducted once the follow-up household survey has been carried out in Karamoja.

KARAMOJA 2016

24

REFERENCES

Angelucci, M. & De Giorgi, G. 2009. Indirect Effects of an Aid Program: How Do Cash Transfers Affect Ineligibles’ Consumption? American Economic Review, 99(1): 486–508.

Angrist, J. D. & Pischke, J. S. 2008. Mostly Harmless Econometrics: An Empiricist’s Companion. Princeton, Princeton University Press.

Card, D. & Krueger, A. 1994. Minimum Wages and Employment: A Case Study of the Fast Food Industry in New Jersey and Pennsylvania, American Economic Review, 84(4): 772–784.

Duflo, E., Glennerster, R. & Kremer, M. 2007. Using randomization in development economics research: A toolkit. In T. Paul Schultz & John A. Strauss, eds. Handbook of Development Economics, pp. 3895–3962.

FAO. 2016. RIMA-II: Resilience Index Measurement and Analysis II. Rome, Italy, FAO. Available at: www.fao.org/3/a-i5665e.pdf

FAO, UNICEF & WFP. 2015. Joint Resilience Strategy for the Karamoja region, Uganda. Rome, WFP.

FAO, UNICEF & WFP. 2016. Dolow. Evidence from the mid-term review of the impact evaluation for the “Building Resilience in Somalia” Joint Strategy. Available at: http://resilienceinsomalia.org/library/impact-evaluations.html

Finnström, S. 2008. Living with bad surroundings: War, history, and everyday moments in Northern Uganda. Durham, Duke University Press.

Gradé, J. T., Tabuti, J. R. S. & Van Damme, P. 2009. Ethnoveterinary knowledge in pastoral Karamoja, Uganda. Journal of Ethnopharmacology, 122(2): 273–293.

Maxwell, D. G. & Caldwell, R. 2008. The coping strategies index: A tool for rapid measurement of household food security and the impact of food aid programming in humanitarian emergencies. Field Methods Manual, 2nd Edition. Available at: http://documents.wfp.org/stellent/groups/public/documents/manual_guide_proced/wfp211058.pdf

Magnani, R. 1999. Sampling Guide. Arlington, Virginia, Food and Nutrition Technical Assistance (FANta) project. Available at: http://pdf.usaid.gov/pdf_docs/Pnacg172.pdf

Miguel, E. & Kremer, M. 2004. Worms: identifying impacts on education and health in the presence of treatment externalities. Econometrica, 72(1): 159–217.

Nannyonjo, J. 2005. Conflicts, poverty and human development in Northern Uganda. The Round Table, 94(381): 473–488.

25


United Nations Office for the Coordination of Humanitarin Affairs (OCHA). 2006. Karamoja ethnic groupings. Available at: http://reliefweb.int/sites/reliefweb.int/files/resources/2CBCB6F1DD3E5AF785257348004A2C0F-ocha_OTS_uga061030.pdf

Rosenbaum, P. R. & Rubin, D. B. 1983. The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1): 41–55.

Saferworld. 2010. Karamoja conflict and security assessment. Saferworld, September 2010.

Swindale, A. & Bilinsky, P. 2006. Household Dietary Diversity Score (HDDS) for Measurement of Household Food Access: Indicator Guide – Version 2. Washington, D.C., USAID. Available at: https://www.fantaproject.org/sites/default/files/resources/HDDS_v2_Sep06_0.pdf

UNICEF and WFP. 2016. Karamoja: Food Security and Nutrition Assessment. Available at: http://vam.wfp.org/CountryPage_assessments.aspx?iso3=UGA

USAID. 2005. Conflict Baseline Study Report Conducted in the Karamajong Cluster of Kenya and Uganda. Available at: http://www.fao.org/fileadmin/user_upload/drought/docs/Karamoja%20Conflict%20Study%202005.pdf

USAID. 2017. Climate Change Risk Profile – Climate Risk Screening for Food Security: Karamoja region, Uganda. Available at: https://www.climatelinks.org/resources/climate-change-risk-profile-climate-risk-screening-food-security-karamoja-region-uganda

All links were checked on 1 October 2017.

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26

ANNEX IESTIMATION OF RCI AND RESILIENCE PILLARS THROUGH RIMA-II METHODOLOGY

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 resilience 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.

In the first step, FA is used to identify the pillars that contribute to household resilience, starting from observed variables. The list and definition of the variables employed in the analysis is shown in Table A1.17 FA is a data reduction technique that relies on finding cross-correlations between the observed variables, identifying the number of (unobservable) factors reflected in those correlations, and predicting the latent outcome (the resilience pillar) as a linear combination of underlying factors. The factors considered for each attribute are those able to explain at least 95 percent of the variable variance. The choice of the resilience pillars employed in this analysis was based on consultations with UBoS and other local experts, as well as literature review and previous analyses (FAO, 2016).

In the second step, a MIMIC model is estimated. This model, which is a type of Structural Equation Model (SEM), is characterized by one underlying latent variable that has multiple indicators as well as multiple causes. In further detail, a system of equations is constructed, specifying the relationships between an unobservable latent variable (the RCI), a set of outcome indicators (the food security indicators), and a set of attributes (the resilience pillars). The MIMIC model is made up of two components, namely the measurement equation (equation 1), reflecting that the observed indicators of food security are imperfect indicators of resilience capacity, and the structural equation (equation 2), which correlates the estimated attributes to the RCI.

17 Table A8 in Annex IV reports the mean values of all variables employed in this report by sampling stratum

27


Table A1. Variables emloyed in the RIMA model

Pillar Variable Definition

ABS

Improved sanitation Variable indicating access to improved toilet facility (private covered pit latrine, private Ventilated Improved Pit (VIP) latrine, private flush toilet).

Improved water Variable indicating access to an improved water source (piped dwelling, piped public tap, protected shallow well, borehole, protected spring, roof rainwater).

Water availability Number of months in a year during which water is available from primary water source.

Closeness to school Index of closeness to primary school. The index ranges between 0 (no access) and 1 (minimum distance in kilometres).

Closeness to hospital / health facility

Index of closeness to hospital/health facility. The index ranges between 0 (no access) and 1 (minimum distance in kilometres).

Closeness to livestock market Index of closeness to livestock market. The index ranges between 0 (no access) and 1 (minimum distance in kilometres).

Closeness to agricultural market

Index of closeness to agricultural market. The index ranges between 0 (no access) and 1 (minimum distance in kilometres).

AST

Wealth index Wealth index is created through FA. A list of variables assumes the value 1 or 0 is used, depending on whether or not a household has specific non-productive assets, such as a television, radio, lamp, etc.

Agricultural asset index Agricultural asset index is created through FA. A list of variables assumes the value 1 or 0 is used, depending on whether or not a household has specific productive assets, such as a tractor, plough, hoe, etc.

TLU TLU standardizes different types of livestock into a single unit of measurement. 18

Land for cropping Total area (acreas) employed for crop production.House value Monetary value (USD) of the house in which the household lives.

SSN

Credit (value) per capita Total amount (USD) of loans received in the last 12 months. Past credit (value) per capita Total amount (USD) of loans contracted prior to the last 12 months.Formal transfers (value) per capita

Total amount (USD) of formal transfers received in the last 12 months. They include cash for work or food for work by NGOs, benefits from old people scheme pay, Social Action Grant funds, scholarships, and social initiatives for elderly.

Informal transfers (value) per capita

Total amount (USD) of informal transfers received in the last 12 months. They include help from family members and in-laws, remittances, gifts and borrowing from friends and relatives.

AC

Average years of education Average years of education of household members.Share of active household members

The share of active household members (>15 and <64 years old) over the number of members of the household.

Coping Strategy Index (CSI) The CSI is a weighted sum of the number of days that the household adopted different strategies 19 to cope with food shortages during the past week.

Number of income-generating activities

Sum of the different sources of income. A list of variables assumes the value 1 or 0 is used, depending on whether or not a household has been involved in farming activity; wage employment; sale of livestock, or their products; non-farm enterprise; a household has received transfers; rent, the sale of assets or other income sources.

Number of crops Sum of different crops cultivated by the households during the last season.

FS

Food consumption per capitaMonetary value (USD) of per capita food consumption, including bought, auto-produced, received for free (as gifts or part of a conditional project) and stored food over the last year.

Household Dietary Diversity Score (HDDS) Number of food groups consumed by the household during the 7 days. 20

18 The conversion factor adopted is: 0.7 camel; 0.5 cattle; 0.3 donkeys /mules; pigs 0.2; 0.1 sheep/goats; 0.01 chickens.19 The strategy weights (numbered 1-4) were established according to the FGDs conducted in Moroto during

the enumerators’ training during November 2016, and are the following: 1) Rely on less preferred or less expensive food – 2; 2) Purchase food on credit – 1; 3) Borrow food, or rely on help from a relative – 2; 4) Gather wild foods, “famine foods” or hunt – 3; 5) Harvest and consume immature crops – 4; 6) Consume seed stock that will be needed for next season – 4; 7) Send household member elsewhere – 3; 8) Limit portion size at meal time – 3; 9) Reduce consumption by adults in order for small children to eat – 2; 10) Reduce consumption by others so working members could eat – 2; 11) Go one entire day without eating – 4; 12) Sell livestock – 3; 13) Reduce number of meals eaten in a day – 3; 14) Beg for food – 3; 15) Selling assets (other than livestock) – 3; 16) Increase the selling of firewood and charcoal – 3; 17) Rely on casual labour – 2; 18) Enrol children in school (even when they are not of school-going age, in order to access food) – 3; 19) Ask for loans from Villages Savings and Loans Associations (VSLAs) and other institutions – 2. The CSI adopted in the resilience estimation is equal to 1/CSI.

20 The food groups considered in the HDDS are the following: cereals, tubers, vegetables, fruits, meat, egg, fish, pulses, milk, oil, sugar, miscellaneous (Swindale and Bilinsky, 2006).

KARAMOJA 2016

28

(1)

(2)

In the formative model, the hypothesis is that resilience (RCI) is influenced by the pillars. Formative indicators are assumed to be correlated and to be measured. In the reflective part, the model’s reflective indicator errors (ε) are correlated and assumed to contain measurement errors. The MIMIC model permits simultaneous estimation of the measurement model and the incorporation of causal variables in the structural model for the latent variable RCI, which is linearly determined by formative indicators or pillars, and the RCI determines the observed reflective indicators.

Since the latent variable (RCI) is inherently unobserved, there is no natural scale or unit of measurement. However, in order to represent it, a reference unit must be defined. Therefore, the coefficient (Λ1 loading) of food consumption is not estimated, but it is restricted to unity, meaning that one standard deviation increase in the RCI results in a single unit increase in the standard deviations of food consumption. This defines the unit of measurement for the other lambda (Λ2) for the HDDS. Given the model above:

(3)

(4)

After estimating the pillars, the RCI is jointly estimated through its pillars and by taking into account the food security indicators. After that, a Min-Max rescaling is employed to bound the index between 0 and 100.21

The results of the MIMIC model are shown in Table A2. The model presents a good fit of the data; all the pillars’ coefficients are positive and statistically significant.

21 The adopted transformation is the following:

(5)

where RCI h is the estimated index for household h.

29


Table A2. MIMIC results

(1)RCI

ABS1.176***

(0.438)

AST2.311***

(0.509)

SSN1.188***

(0.367)

AC4.028***

(0.551)

Food consumption per capita

1(0)

HDDS0.098***

(0.013)

Chi 2 23.04TLI 0.893CFI 0.964RMSEA 0.053pclose 0.363

Observations 2 380Standard errors in parentheses.

*** p<0.01, ** p<0.05, * p<0.1

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30

ANNEX IISAMPLING WEIGHTS

Table A3. Sampling weights at subcounty level

District County Subcounty

N. households

from Uganda Census

N. of households interviewed by stratum Weights

Treatment Spillover

(direct and indirect)

Pure control

Different ethnicity

No. of households interviewed

/ N. households

located in the

subcountyABIM 18 297 200

LABWOR ABIM 2 114 23.11 0.0109 LABWOR ABIM TOWN

COUNCIL2 980 32.57 0.0109

LABWOR ALEREK 2 821 31 0.0109 LABWOR LOTUKEI 3 529 39 0.0109 LABWOR MORULEM 4 165 46 0.0109 LABWOR NYAKWAE 2 688 29 0.0109

AMUDAT 15 850 200 POKOT AMUDAT 4 305 54 0.0126 POKOT AMUDAT TOWN

COUNCIL2 292 29 0.0126

POKOT KARITA 4 822 61 0.0126 POKOT LOROO 4 431 56 0.0126

KAABONG 29 725 400 DODOTH KAABONG EAST 1 627 22 22 0.0135 DODOTH KAABONG TOWN

COUNCIL2 292 31 31 0.0135

DODOTH KAABONG WEST 2 353 32 32 0.0136 DODOTH KALAPATA 4 152 56 56 0.0135 DODOTH KAMION 1 170 16 16 0.0137 DODOTH KAPEDO 2 576 35 35 0.0136 DODOTH KATHILE 3 569 48 48 0.0134 DODOTH KAWALAKOL 2 210 30 30 0.0136 DODOTH LOBALANGIT 1 416 19 19 0.0134 DODOTH LODIKO 1 294 17 17 0.0131 DODOTH LOLELIA 1 399 19 19 0.0136 DODOTH LOYORO 676 9 9 0.0133 DODOTH NAPORE

(KARENGA)2 521 34 33 0.0131

DODOTH SIDOK 2 470 33 33 0.0134

31


District County Subcounty

N. households

from Uganda Census

N. of households interviewed by stratum Weights

Treatment Spillover

(direct and indirect)

Pure control

Different ethnicity

No. of households interviewed

/ N. households

located in the

subcountyKOTIDO 26 847 300

JIE KACHERI 5 129 57 0.0112 JIE KOTIDO 4 849 54 0.0112 JIE KOTIDO TOWN

COUNCIL2 827 32 0.0112

JIE NAKAPERIMORU 3 427 38 0.0112 JIE PANYANGARA 5 622 63 0.0112 JIE RENGEN 4 993 56 0.0112

MOROTO 29 130 340 150 MATHENIKO KATIKEKILE 2 238 56 0.0249 MATHENIKO NADUNGET 7 929 197 0.0249 MATHENIKO RUPA 5 018 42 0.0083 MATHENIKO TAPAC 3 492 87 0.0249 MOROTO MUNICIPALITY

RUPA 5 018 42 0.0083

MOROTO MUNICIPALITY

NADUNGET 7 929 66 0.0083

NAKAPIRIPIRIT 26 414 300CHEKWII KAKOMONGOLE 3 048 35 0.0114 CHEKWII LOREGAE 4 484 51 0.0114 CHEKWII MORUITA 2 528 29 0.0114 CHEKWII NAKAPIRIPIRIT

TOWN COUNCIL945 11 0.0114

CHEKWII NAMALU 5 209 59 0.0114 PIAN LOLACHAT 4 312 49 0.0114 PIAN LORENGEDWAT 1 433 16 0.0114 PIAN NABILATUK 4 455 51 0.0114

NAPAK 27 471 340 150 BOKORA IRIIRI 7 760 66 0.0085 BOKORA LOKOPO 4 139 142 0.0344 BOKORA LOPEEI 2 320 80 0.0344 BOKORA LORENGECORA 1 997 17 0.0085 BOKORA LOTOME 2 345 20 0.0085 BOKORA MATANY 4 400 38 0.0085 BOKORA NAPAK TOWN

COUNCIL1 086 9 0.0085

BOKORA NGOLERIET 3 424 118 0.0344

Table A3. Sampling weights at subcounty level (cont.)

KARAMOJA 2016

32

ANNEX IIIREGRESSION MODELS: PROBABILITY OF BEING A TREATMENT GROUP HOUSEHOLD VERSUS HOUSEHOLD FROM A DIFFERENT STRATUM

Table A4. Linear Probability Model (LPM) and Probit model of being treatment group household vs pure control group household

LPM Probit

(1) (2) (3) (4)Food security indicators

Food consumption -0.001*** -0.003*(0.0003) (0.002)

HDDS 0.063*** 0.318***(0.007) (0.041)

ABS

Improved sanitation -0.400*** -0.372*** -1.590*** -1.526***(0.030) (0.029) (0.146) (0.151)

Improved water 0.408*** 0.356*** 1.660*** 1.547***(0.047) (0.045) (0.228) (0.239)

Water availability 0.019** 0.011 0.068* 0.029(0.008) (0.008) (0.038) (0.039)

Closeness to school -0.578*** -0.537*** -2.454*** -2.296***(0.081) (0.078) (0.411) (0.419)

Closeness to hospital 0.002 0.0027 0.040 -0.002(0.031) (0.029) (0.171) (0.206)

Closeness to livestock market -0.215** -0.184** -2.405*** -2.599***(0.097) (0.093) (0.817) (0.942)

Closeness to agricultural market 0.039 0.024 0.727* 0.736(0.054) (0.052) (0.417) (0.619)

AST

Wealth index -0.211*** -0.255*** -0.958*** -1.245***(0.063) (0.061) (0.309) (0.329)

Agricultural asset index -0.331*** -0.316*** -1.505*** -1.437***(0.095) (0.092) (0.451) (0.472)

TLU -0.005 -0.004 -0.016 -0.009(0.007) (0.007) (0.033) (0.037)

Land for cropping 0.021*** 0.025*** 0.106*** 0.118***(0.006) (0.006) (0.031) (0.033)

House value -0.0003*** -0.0002*** -0.002*** -0.002***(0.0001) (0.0001) (0.001) (0.001)

33


LPM Probit

(1) (2) (3) (4)SSN

Credit pc 0.005** 0.003 0.036** 0.018(0.002) (0.002) (0.015) (0.017)

Past credit pc -0.005 -0.003 -0.035 -0.019(0.005) (0.004) (0.024) (0.026)

Formal transfers pc 0.003*** 0.003*** 0.027*** 0.029***(0.001) (0.001) (0.008) (0.008)

Informal transfers pc -0.001 0.0001 -0.005 -0.002(0.002) (0.002) (0.011) (0.012)

AC

Average education -0.012*** -0.012*** -0.054*** -0.064***(0.004) (0.004) (0.020) (0.021)

Share of active household members -0.248** -0.189* -0.939* -0.809(0.108) (0.107) (0.538) (0.571)

CSI (inverse) -0.159*** -0.177*** -0.650** -0.722**(0.053) (0.051) (0.273) (0.287)

N. income sources 0.013 -0.011 0.083 -0.007(0.012) (0.011) (0.056) (0.060)

N. cultivated crops -0.021** -0.032*** -0.153*** -0.229***(0.011) (0.010) (0.055) (0.059)

Self-reported shocks

Drought 0.060 0.068* 0.194 0.256(0.039) (0.038) (0.186) (0.198)

Flood -0.102 -0.095 -0.296 -0.184(0.066) (0.064) (0.320) (0.344)

Pests, parasites and diseases -0.107*** -0.097*** -0.454*** -0.386**(0.033) (0.031) (0.158) (0.170)

Low crop / livestock product prices -0.247** -0.147 -1.088** -0.673(0.108) (0.104) (0.550) (0.566)

High input / services prices 0.029 -0.015 0.114 -0.091(0.064) (0.062) (0.338) (0.387)

High food prices -0.124*** -0.111*** -0.462*** -0.467***(0.034) (0.032) (0.160) (0.171)

Business failure -0.021 -0.049 -0.377 -0.519(0.081) (0.078) (0.463) (0.464)

Severe illness / injury 0.070** 0.050 0.349* 0.224(0.035) (0.034) (0.187) (0.196)

Resource-based conflict / communal / political crisis 0.269* 0.190 0.986 0.887(0.150) (0.144) (1.057) (1.438)

Other shocks -0.091* -0.080* -0.551** -0.544**(0.048) (0.046) (0.221) (0.231)

Household characteristics

Number of male adults 0.013 0.011 0.062 0.090(0.017) (0.017) (0.082) (0.089)

Number of female adults -0.014 -0.022 -0.094 -0.118(0.015) (0.015) (0.073) (0.077)

Number of children -0.045*** -0.049*** -0.196*** -0.228***(0.011) (0.011) (0.054) (0.057)

Female HH -0.009 0.007 -0.074 0.048(0.030) (0.029) (0.149) (0.156)

Livelihood dummies

Agro-pastoralist -0.124*** -0.128*** -0.587*** -0.621***(0.025) (0.024) (0.121) (0.127)

Other livelihood -0.321*** -0.310*** -1.830*** -1.779***(0.048) (0.046) (0.285) (0.284)

Constant 0.606*** 0.496*** 0.710 0.075(0.132) (0.129) (0.619) (0.647)

Observations 1 064 1 064 1 064 1 064R-squared 0.490 0.529

Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1

Table A4. Linear Probability Model (LPM) and Probit model of being treatment group household vs pure control group household (cont.)

KARAMOJA 2016

34

Figure A1. Distribution of the propensity score: treatment group household vs pure control group household

(A) Distribution of Propensity score from model (3) in Table A4

0

1

2

3

4

Den

sity

0 0.2 0.4 0.6 0.8 1

Propensity score

Target

Pure control

(B) Distribution of Propensity score from model (4) in Table A4

0

1

2

3

4

Den

sity

0 0.2 0.4 0.6 0.8 1

Propensity score

Target

Pure control

5

Source:Own elaboration with Stata 14

35


Table A5. Linear Probability Model (LPM) and Probit model of being treatment group household vs direct spillover group household

LPM Probit


Food consumption -0.001 -0.002(0.001) (0.002)

HDDS -0.034*** -0.105***(0.009) (0.029)

ABS

Improved sanitation -0.110* -0.104* -0.261 -0.255(0.059) (0.058) (0.182) (0.186)

Improved water 0.211** 0.206** 0.671** 0.667**(0.094) (0.093) (0.284) (0.286)

Water availability 0.028** 0.032*** 0.079** 0.094***(0.011) (0.011) (0.034) (0.035)


Closeness to hospital 0.104 0.110 0.639 0.708(0.077) (0.076) (0.486) (0.505)

Closeness to livestock market -0.487*** -0.489*** -1.806** -1.731**(0.188) (0.186) (0.703) (0.696)

Closeness to agricultural market -0.046 -0.013 -0.456 -0.378(0.176) (0.174) (0.634) (0.636)

AST

Wealth index -0.109 -0.039 -0.310 -0.088(0.095) (0.095) (0.296) (0.305)

Agricultural asset index -0.023 0.008 -0.174 -0.108(0.124) (0.123) (0.384) (0.390)

TLU 0.018 0.014 0.070 0.058(0.013) (0.012) (0.045) (0.045)

Land for cropping 0.009 0.009 0.032 0.033(0.008) (0.008) (0.026) (0.026)

House value -0.00002 0.00003 -0.0001 0.00003(0.0002) (0.0002) (0.001) (0.001)

SSN

Credit pc 0.019*** 0.019*** 0.070*** 0.070***(0.007) (0.007) (0.026) (0.025)

Past credit pc -0.012 -0.014 -0.044 -0.045(0.011) (0.010) (0.036) (0.036)

Formal transfers pc 0.001 0.001 0.002 0.004(0.001) (0.001) (0.005) (0.005)

Informal transfers pc 0.003 0.002 0.012 0.010(0.003) (0.003) (0.010) (0.010)

AC

Average education -0.001 -0.001 -0.005 -0.002(0.006) (0.006) (0.017) (0.018)

Share of active household members 0.202 0.291** 0.716 1.013**(0.135) (0.140) (0.439) (0.466)

CSI (inverse) -0.054 -0.025 -0.194 -0.120(0.085) (0.084) (0.266) (0.270)

N. income sources 0.013 0.030** 0.051 0.103**(0.015) (0.015) (0.047) (0.048)

N. cultivated crops 0.028* 0.034** 0.082 0.105*(0.017) (0.017) (0.053) (0.054)

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36

LPM Probit

(1) (2) (3) (4)Self-reported shocks

Drought 0.060 0.055 0.192 0.162(0.058) (0.058) (0.179) (0.179)

Flood -0.218*** -0.216*** -0.609** -0.620**(0.080) (0.079) (0.245) (0.247)

Pests, parasites and diseases 0.079 0.081 0.256 0.268(0.055) (0.055) (0.184) (0.184)

Low crop / livestock product prices0.219 0.171

(0.202) (0.200)

High input / services prices -0.225*** -0.152** -0.684*** -0.448*(0.075) (0.075) (0.228) (0.237)

High food prices -0.032 -0.012 -0.126 -0.064(0.049) (0.049) (0.152) (0.156)

Business failure -0.106 -0.098 -0.333 -0.322(0.128) (0.127) (0.387) (0.394)

Severe illness / injury 0.053 0.054 0.166 0.168(0.049) (0.049) (0.155) (0.157)

Resource-based conflict / communal / political crisis0.260 0.323

(0.232) (0.230)

Other shocks 0.138 0.123 0.273 0.235(0.086) (0.085) (0.265) (0.265)


Number of male adults -0.028 -0.048** -0.087 -0.151**(0.022) (0.023) (0.070) (0.075)

Number of female adults -0.036* -0.047** -0.115* -0.148**(0.020) (0.021) (0.064) (0.069)

Number of children -0.010 -0.008 -0.030 -0.024(0.015) (0.015) (0.046) (0.048)

Female HH -0.054 -0.069* -0.145 -0.197*(0.038) (0.037) (0.116) (0.118)

Livelihood dummies

Agro-pastoralist -0.030 -0.009 -0.100 -0.033(0.032) (0.032) (0.100) (0.103)

Other livelihood -0.069 -0.067 -0.171 -0.172(0.111) (0.110) (0.329) (0.336)

Constant 0.072 0.190 -1.291** -0.954(0.193) (0.192) (0.579) (0.588)

Observations 974 974 965 965R-squared 0.134 0.155


Table A5. Linear Probability Model (LPM) and Probit model of being treatment group household vs direct spillover group household (cont.)

37


Figure A2. Distribution of the propensity score: treatment group household vs direct spillover group household

(A) Distribution of Propensity score from model (3) in Table A5

0

1

2

3

4

Den

sity

0 0.2 0.4 0.6 0.8 1

Propensity score

Target

Direct spillover

(B) Distribution of Propensity score from model (4) in Table A5

0

1

2

3

4

Den

sity

0 0.2 0.4 0.6 0.8 1

Propensity score

Target

Direct spillover


KARAMOJA 2016

38

Table A6. Linear Probability Model (LPM) and Probit model of being a treatment group household vs indirect spillover group household

LPM Probit


Food consumption -0.001* -0.002*(0.0003) (0.001)

HDDS 0.020*** 0.073***(0.007) (0.027)

ABS


Improved water 0.242*** 0.238*** 0.893*** 0.871***(0.060) (0.059) (0.249) (0.249)

Water availability 0.016 0.014 0.051 0.046(0.010) (0.010) (0.038) (0.038)


Closeness to hospital 0.073 0.065 0.293 0.284(0.061) (0.061) (0.257) (0.260)

Closeness to livestock market -0.286*** -0.291*** -3.012*** -3.017***(0.107) (0.107) (0.822) (0.829)


AST

Wealth index 0.181** 0.163** 0.518* 0.458(0.079) (0.080) (0.288) (0.292)

Agricultural asset index 0.037 0.017 0.163 0.082(0.105) (0.105) (0.390) (0.391)

TLU 0.004 0.003 0.005 0.003(0.010) (0.010) (0.038) (0.038)


House value -0.001*** -0.001*** -0.003*** -0.003***(0.0001) (0.0001) (0.0004) (0.0004)

SSN

Credit pc 0.001 -0.0001 0.016 0.012(0.004) (0.004) (0.017) (0.017)

Past credit pc -0.007** -0.007** -0.050** -0.046*(0.003) (0.003) (0.024) (0.024)

Formal transfers pc 0.003** 0.003** 0.012*** 0.013***(0.001) (0.001) (0.005) (0.005)

Informal transfers pc 0.003* 0.003* 0.013* 0.013*(0.002) (0.002) (0.007) (0.007)

AC

Average education 0.003 0.002 0.014 0.014(0.004) (0.004) (0.017) (0.017)

Share of active household members 0.050 0.099 -0.011 0.193(0.112) (0.116) (0.428) (0.444)

CSI (inverse) 0.069 0.052 0.111 0.057(0.071) (0.071) (0.264) (0.266)

N. income sources -0.020* -0.025** -0.060 -0.078*(0.012) (0.012) (0.044) (0.046)

N. cultivated crops -0.036*** -0.038*** -0.150*** -0.159***(0.013) (0.013) (0.048) (0.048)

39


LPM Probit


Drought -0.084* -0.078* -0.292* -0.281(0.046) (0.046) (0.173) (0.174)

Flood -0.295*** -0.299*** -1.153*** -1.166***(0.049) (0.049) (0.204) (0.204)

Pests, parasites and diseases -0.305*** -0.309*** -1.121*** -1.138***(0.031) (0.031) (0.121) (0.122)

Low crop / livestock product prices -0.059 -0.067 -0.617 -0.630(0.105) (0.105) (0.531) (0.512)

High input / services prices 0.312*** 0.297*** 1.187*** 1.132***(0.075) (0.075) (0.333) (0.333)

High food prices -0.098*** -0.093*** -0.337*** -0.321**(0.032) (0.032) (0.123) (0.125)

Business failure 0.159 0.160 0.656 0.676(0.111) (0.111) (0.471) (0.475)

Severe illness / injury -0.085** -0.086** -0.275** -0.287**(0.035) (0.035) (0.133) (0.133)

Resource-based conflict / communal / political crisis0.377* 0.344*

(0.208) (0.208)

Other shocks 0.028 0.019 0.122 0.077(0.067) (0.067) (0.247) (0.247)


Number of male adults -0.020 -0.026 -0.056 -0.083(0.017) (0.018) (0.063) (0.066)

Number of female adults 0.006 -0.002 0.024 -0.012(0.017) (0.017) (0.064) (0.067)

Number of children -0.017 -0.020* -0.070 -0.083*(0.012) (0.012) (0.044) (0.045)

Female HH 0.049 0.054* 0.203* 0.230*(0.032) (0.032) (0.122) (0.123)

Livelihood dummies

Agro-pastoralist -0.018 -0.015 -0.082 -0.060(0.026) (0.026) (0.098) (0.099)

Other livelihood -0.325*** -0.327*** -1.235*** -1.236***(0.054) (0.054) (0.238) (0.237)

Constant 0.326** 0.287* -0.561 -0.692(0.153) (0.155) (0.570) (0.580)



Table A6. Linear Probability Model (LPM) and Probit model of being a treatment group household vs indirect spillover group household (cont.)

KARAMOJA 2016

40

Figure A3. Distribution of the propensity score: treatment group household vs indirect spillover group household

(A) Distribution of Propensity Score from model (3) in Table A6

0

0.5

1

1.5

2

Den

sity

0 0.2 0.4 0.6 0.8 1

Propensity score

Target

Indirect spillover

(B) Distribution of Propensity Score from model (4) in Table A6

0

0.5

1

1.5

2

Den

sity

0 0.2 0.4 0.6 0.8 1

Propensity score

Target

Indirect spillover


41


Table A7. Linear Probability Model (LPM) and Probit model of being a treatment group household vs different ethnicity group household

LPM Probit


Food consumption -0.002*** -0.012***(0.0003) (0.002)

HDDS 0.039*** 0.209***(0.006) (0.039)

ABS


Improved water 0.159*** 0.138*** 0.846*** 0.671**(0.050) (0.048) (0.283) (0.284)

Water availability 0.055*** 0.047*** 0.223*** 0.204***(0.008) (0.008) (0.045) (0.048)


Closeness to hospital 0.020 0.013 -0.017 -0.078(0.058) (0.056) (0.273) (0.291)

Closeness to livestock market -0.055 -0.081 -0.338 -0.435(0.100) (0.097) (0.654) (0.695)


AST

Wealth index -0.362*** -0.373*** -1.626*** -1.677***(0.063) (0.061) (0.332) (0.351)

Agricultural asset index 0.082 0.122 0.282 0.603(0.098) (0.095) (0.534) (0.547)

TLU -0.012** -0.004 -0.069* -0.042(0.006) (0.006) (0.037) (0.040)


House value -0.001*** -0.001*** -0.003*** -0.003***(0.0001) (0.0001) (0.001) (0.001)

SSN

Credit pc -0.0002 0.0003 -0.013 -0.029(0.003) (0.002) (0.019) (0.020)

Past credit pc -0.003 -0.003 0.010 0.020(0.005) (0.005) (0.027) (0.028)

Formal transfers pc 0.002* 0.002* 0.015** 0.016**(0.001) (0.001) (0.006) (0.006)

Informal transfers pc 0.0003 0.0004 -0.002 -0.001(0.002) (0.002) (0.009) (0.009)

AC

Average education -0.006 -0.006 -0.025 -0.027(0.004) (0.004) (0.021) (0.022)

Share of active household members -0.137 0.082 -0.998* 0.290(0.099) (0.100) (0.561) (0.610)

CSI (inverse) -0.075 -0.076 -0.223 -0.196(0.054) (0.052) (0.285) (0.305)

N. income sources 0.019* 0.008 0.137** 0.094(0.011) (0.011) (0.061) (0.066)

N. cultivated crops -0.061*** -0.073*** -0.445*** -0.526***(0.010) (0.009) (0.061) (0.066)

KARAMOJA 2016

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LPM Probit


Drought 0.077** 0.082** 0.344* 0.379*(0.039) (0.038) (0.192) (0.201)

Flood -0.164*** -0.150*** -0.767*** -0.661**(0.051) (0.050) (0.281) (0.292)

Pests, parasites and diseases -0.310*** -0.290*** -1.364*** -1.372***(0.027) (0.026) (0.147) (0.154)

Low crop / livestock product prices -0.148** -0.114 -2.072*** -2.258***(0.074) (0.071) (0.630) (0.594)

High input / services prices 0.142** 0.111* 0.768* 0.641(0.060) (0.058) (0.426) (0.425)

High food prices -0.083*** -0.061** -0.342** -0.298*(0.031) (0.030) (0.165) (0.169)

Business failure 0.087 0.065 0.408 0.275(0.086) (0.083) (0.477) (0.468)

Severe illness / injury -0.093*** -0.083*** -0.379** -0.399**(0.032) (0.031) (0.164) (0.171)

Resource-based conflict / communal / political crisis 0.183 0.142 1.207 1.077(0.143) (0.138) (0.820) (0.954)

Other shocks 0.187*** 0.173*** 0.866** 0.828**(0.067) (0.065) (0.362) (0.384)


Number of male adults 0.008 -0.029* 0.057 -0.138(0.015) (0.015) (0.080) (0.089)

Number of female adults 0.016 -0.015 0.068 -0.071(0.015) (0.015) (0.085) (0.093)

Number of children -0.018* -0.029*** -0.136** -0.161***(0.011) (0.010) (0.058) (0.060)

Female HH -0.011 -0.009 -0.042 -0.046(0.029) (0.028) (0.163) (0.172)

Livelihood dummies

Agro-pastoralist -0.093*** -0.076*** -0.471*** -0.368***(0.024) (0.023) (0.130) (0.138)

Other livelihood -0.167** -0.128* -0.428 -0.295(0.074) (0.072) (0.370) (0.384)

Constant 0.070 0.155 -1.815** -1.833**(0.126) (0.123) (0.724) (0.758)



Table A7. Linear Probability Model (LPM) and Probit model of being a treatment group household vs different ethnicity group household (cont.)

43


Figure A4. Distribution of the propensity score: treatment group household vs different ethnicity group household

(A) Distribution of Propensity Score from model (3) in Table A7

0

1

2

3

4

5

Den

sity

0 0.2 0.4 0.6 0.8 1

Propensity score

Target

Different ethnicity

(B) Distribution of Propensity Score from model (4) in Table A7

0

1

2

3

4

5

Den

sity

0 0.2 0.4 0.6 0.8 1

Propensity score

Target

Different ethnicity

6


KARAMOJA 2016

44

ANNEX IVSUMMARY STATISTICS: MEAN VALUES OF VARIABLES BY SAMPLING STRATUM

Table A8. Mean values of pillars’ variables, food security indicators, shocks and controls by sampling stratum


Indirect spillover

Different ethnicity

Pure control

Pillars’ variables Improved sanitation 0.054 0.096 0.127 0.198 0.449Improved water 0.980 0.965 0.937 0.881 0.871N. months water availability 11.595 11.380 11.529 10.837 11.357Closeness to primary school 0.065 0.140 0.104 0.100 0.135Closeness to hospital / health facility 0.061 0.054 0.053 0.068 0.121Closeness to livestock market 0.021 0.056 0.044 0.027 0.097Closeness to agricultural market 0.034 0.059 0.074 0.039 0.104Wealth index 0.113 0.115 0.115 0.264 0.218Agricultural asset index 0.255 0.251 0.233 0.229 0.264TLU 0.671 0.459 0.663 1.448 0.754Land 3.706 3.195 2.275 2.250 3.721House value 47.739 50.852 104.938 143.052 108.833Credit pc 1.042 0.416 1.240 2.023 1.608Past credit pc 0.311 0.208 0.908 0.476 0.672Formal transfers pc 4.657 3.835 2.551 1.861 1.456Informal transfers pc 1.079 1.032 0.995 1.287 1.597Average education 1.640 1.772 1.807 2.561 2.521Share of active household members 0.482 0.446 0.460 0.487 0.476CSI (inverse) 0.067 0.069 0.062 0.133 0.147Number of income-generating activities 2.256 2.176 2.394 2.473 2.459Number of cultivated crops 2.477 2.208 2.147 2.661 2.938Food security indicatorsFood consumption pc 58.249 60.103 57.103 82.169 54.021HDDS 6.035 6.527 5.965 6.106 5.231Shocks (dummies)Drought 0.915 0.879 0.907 0.866 0.804Flood 0.024 0.061 0.105 0.097 0.045Pests, parasites and diseases 0.104 0.064 0.339 0.517 0.248Low crop / livestock product prices 0.008 0.000 0.018 0.059 0.017High input / services prices 0.036 0.064 0.017 0.050 0.032High food prices 0.110 0.115 0.245 0.260 0.251Business failure 0.011 0.019 0.012 0.027 0.037

45



Indirect spillover

Different ethnicity

Pure control

Severe illness / injury 0.109 0.093 0.145 0.176 0.136Job loss 0.002 0.000 0.007 0.015 0.000Resource-based conflict / communal / political crisis 0.006 0.000 0.000 0.005 0.007Other shocks 0.038 0.038 0.032 0.015 0.132Household control characteristicsNumber of male adults 1.222 1.179 1.346 1.371 1.362Number of female adults 1.501 1.546 1.422 1.423 1.682Number of children 2.694 2.974 3.135 3.064 3.337Female HH 0.245 0.297 0.164 0.158 0.191Agro-pastoralist livelihood (self-reported)22 0.449 0.425 0.419 0.579 0.538Farmer livelihood (self-reported) 0.537 0.546 0.471 0.379 0.268Other livelihood (self-reported) 0.014 0.029 0.110 0.042 0.194

Observations 661 313 599 404 403

22 The household classification by livelihood is based on self-reported information. The frequency of the disaggregated answers is the following: Pastoralist – 82; Agro-Pastoralist – 1 050; Farmer – 1 069; Fishing – 1; Urban – 35; Entrepreneur – 46; Mixed – 89; Other - 8. The answers have been aggregated as follows: Agro-pastoralist (Pastoralist, Agro-Pastoralist); Farmer (Farmer); Other (Fishing, Urban, Entrepreneur, Mixed, Other).

This report is part of the impact evaluation of the joint resilience strategy of FAO, UNICEF and WFP in Karamoja, Uganda. It has been prepared under the Resilience Measurement Unit (RMU) pooling together the Office of Prime Minister, the Uganda Bureau of Statistics, WFP, UNICEF and FAO.

This document will be followed, during next years, by mid-term and/or final rports.

The impact evaluation aims at providing and programming policy guidance to policy makers, practitioners, UN agencies, NGO and other stakeholders by identifying the key factors that contribute to the growth of household resilience.

It also seeks to identify the positive or negative impact of the joint resilience strategy.

The analysis is largely based on the use of the FAO Resilience Index Measurement and Analysis (RIMA) tool.

Contacts: Luca Russo, FAO Senior Economist – [email protected] d’Errico, FAO Economist – [email protected] team – [email protected] I8021EN/1/10.17

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