PSNP Variable Levels of Support_FINAL

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Model for Guidance to Woreda-Level Officials on Variable Levels of Support to

Beneficiaries in the ProductiveSafety Nets Program (PSNP) Using the Livelihoods

Integration Unit (LIU) Database1

Final Report

Prepared for the World Bank

14 November, 2008

1The LIU is a USAID-funded project.


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Executive Summary

Objectives

The overarching objective of this analysis is to identify the possible contribution of the

Livelihoods Integration Unit (LIU) livelihoods data to the development of a practical methodology

to determine the level of support that the PSNP should provide to beneficiary households in fourpilot woredas in Amhara, Tigray, Oromiya, and SNNPR regions. More specifically, the objectiveof the analysis is to determine the variable levels of support defined by levels of duration of

support (i.e. 3, 6, or 9 months) from the PSNP required by different wealth groups within eachworeda and livelihood zone.

Methodology

The LIU database contains information on total income (food + cash) for four wealth groups (very

poor, poor, middle, and better-off) in each livelihood zone in a defined recent reference year.When compared to a defined income threshold, this can be used to estimate the total deficit faced

by each wealth group. The LIU uses two different thresholds: a) the survival threshold and b) thelivelihoods protection threshold2. Analysis results on the extent of deficit faced by each wealth

group can be converted into population facing different durations of deficit, as indicated above.Population falling under each duration of deficit can then be calculated for each wealth group,livelihood zone, and woreda.

Two different scenario types have been developed to identify PSNP requirements in the four pilotworedas. This has been done to accommodate, on the one hand, the aims of the PSNP program,

which is intended to address chronic food insecurity, and on the other hand, the short-term realitiesfacing the World Banks pilot of variable levels of support in a context of abnormally high food

price inflation and corresponding expectations of forthcoming support at woreda level through thepilot program.

For the purposes of the former addressing the overarching aims of the PSNP program in thelonger term in order to establish a comparable and accurate measure of chronic food insecurity

which the PSNP aims to mitigate, it is necessary to assess beneficiary numbers for an averageyear. Average crop production and market price levels have been calculated for the last seven

years, from the production years of 2000/01 through 2006/07. These data on average yearconditions have then been compared to reference year crop and market price conditions. Average

year total income of has then been projected from the reference year baseline data.

For the purposes of the later addressing the operational challenges of the pilot program in the

short term beneficiary numbers have been calculated for a theoretical year involving cropproduction and market price levels closer to current year conditions.

Key Findings

In all of the pilot woredas in an average year, PSNP support provides the levels of support

required to cover both the survival deficit or short-term absolute deficit of food and thelivelihoods protection deficit required to protect assets. Although PSNP is not necessarily

intended to build assets, it may have the potential to do so on a limited scale where current actual

2Please see Annex B for further detail on the survival and livelihoods protection thresholds.


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PSNP support covers far more than the livelihoods protection needs of households, as inEndamehoni woreda.

Recommendations

A. LIU Data Can be Used to Strengthen PSNP Targeting Among Woredas

Woredas receiving PSNP support should be assessed within their regional and national context in

order to ensure that support needs are appropriately matched with support allocation. Using theLIU database on livelihoods and food security allows for such comparative analysis. Findings

show, for instance, that Endamehoni woreda may not be typical of the rest of Tigray region as itsupports household populations with total income (food + cash) levels that are above the regional

average. Analysis indicates that, in an average year, no population faces a livelihoods protectiondeficit greater than 1.5 months in this woreda, despite PSNP support to 28% of the population for

6 months.

B. LIU Data Can Be Used To Strengthen PSNP Targeting Within Woredas

It is clear from HEA analysis that some livelihood zones are worse off than others within the sameworeda. Comparisons of HEA and PSNP beneficiaries indicates that targeting could be improved

within woredas by redirecting some PSNP support resources from PAs3

in livelihood zones wheresupport covers well above livelihoods protection requirements to PAs in livelihood zones that are

worse off within the woreda but that do not receive sufficient PSNP support to cover livelihoodsprotection requirements. A small reallocation of resources within woredas would be appropriate

for all pilot woredas assessed in this analysis.

C. LIU Databases Can Provide Tools to Support The Allocation of Variable Levels of PSNPSupport

Tools would provide the type of information presented in this report to enable officials to identify:

a) Where PSNP resources should be targeted within regions e.g. woredas with high food-insecure populations;

b) Where PSNP resources should be targeted within woredas e.g. livelihood zones withproportions of population falling under the livelihoods protection deficit;

c) Number of beneficiaries who should receive each level of support (e.g. 3 months, 6months, etc), disaggregated by wealth group and within each woreda livelihood zone;

d) Household asset levels at each support level cut-off to facilitate identification ofhouseholds at community level who should receive different levels of support.

3A PA, or Peasant Association (also called kebele), is the smallest administrative unit in Ethiopia and is

one level smaller in scale than a woreda, or district.


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Table of Contents

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I. Introduction

The main objective of this analysis is to identify the possible contribution of the LivelihoodsIntegration Unit (LIU) livelihoods data to the development of a practical methodology to

determine the level of support that the PSNP should provide to beneficiary households in fourpilot woredas in Amhara, Tigray, Oromiya, and SNNPR regions. More specifically, the objective

of the analysis is to determine the variable levels of support defined by levels of duration ofsupport (i.e. 3, 6, or 9 months) from the PSNP required by different wealth groups within each

woreda and livelihood zone.

This analysis is to be incorporated into a larger study on variable levels of PSNP support carried

out by Dadimos Development Consultants that aims to determine whether it is possible to provideworedas with a quota for the number of households and/or population to be provided with

different levels of PSNP transfers, as indicated above, rather than the uniform six months4 ofsupport provided currently. The analysis therefore seeks to contribute to the process of

strengthening PSNP targeting to most effectively allocate adequate resources to those householdsmost in need.

4Eight months of support exists in a limited number of woredas as well.


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II. Discussion of Methods and Data Used in the Analysis

A. Using the LIU data to estimate populations falling under different levels of deficit

The LIU database contains information on total income (food + cash) for four wealth groups (very

poor, poor, middle and better-off) in each livelihood zone in a defined recent reference year. When

compared to a defined income threshold, this can be used to estimate the deficit faced by eachwealth group. The LIU uses two different thresholds: a) the survival threshold and b) thelivelihoods protection threshold5. An analysis can be run for each type of deficit the survival

deficit and the livelihoods protection deficit.

Analysis results on the extent of deficit faced by each wealth group can be converted into

population facing different durations of deficit. Population falling under each duration of deficitcan then be calculated for each wealth group, livelihood zone, and woreda. See Annex A for a

more detailed explanation of how the analysis is done.

A wide range of scenarios can be run with LIU Livelihoods Impact Analysis Sheets (LIAS) to

assess the impact of changes in crop production, market prices, livestock holdings, labour rates,

population increases, and so forth, on the food security status of populations in woredas andlivelihood zones. Scenario data which can be either real, as for seasonal assessments, ortheoretical, e.g. in order to project future conditions on the above parameters is compared withreference year conditions. Total income (food + cash) in the scenario year can thus be projected

from the reference year baseline data.

B. Scenarios Used to Assess PSNP Beneficiary Numbers in Pilot Woredas

Two different scenario types were developed to identify PSNP requirements in the four pilot

woredas. This was done to accommodate, on the one hand, the aims of the PSNP program, whichis intended to address chronic food insecurity, and on the other hand, the short-term realities

facing the World Banks pilot of variable levels of support in the four pilot woredas. Scenarios

are as follows:

Scenario A: Assessment of beneficiary numbers in an average year, from 2000/01through 2006/07 in order to assess chronic food insecurity of populations

Scenario B: Assessment of beneficiary numbers in a theoretical year involving crop production and market price levels closer to current year conditions to address the

realities facing woreda administrations in a year of high needs and expectations facing thepilot program.

i) Assessing Beneficiary Numbers for Scenario A: an Average Year

The PSNP is designed to cover needs of the chronically food insecure. Chronic food insecurity

can be defined in several ways. For the purposes of this scenario analysis, a working definition isdefined as the duration of deficit that households face in an average year. Analysis resultspresented here identify the number of households that fall under the following durations of deficit:

5See Annex B for an explanation of these thresholds.


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Table 1. Ranges of Deficit Duration Used in the Analysis

Average duration of

assistance (months)

Associated range of

deficits (months)

0 Less than 1.5

3 1.5 to 4.5

6 4.5 to 7.5

9 7.5 to 10.5

Analysis results on the extent of deficit faced by each wealth group6 can be converted into

population facing the range of deficit months as outlined above. Population falling under eachduration of deficit can then be calculated for each wealth group, livelihood zone, and woreda.

Please see Annex A for further detail on how the LIU data has been used to estimate thepercentage of population facing different levels of analysis.

In order to establish a comparable and accurate measure of chronic food insecurity which thePSNP aims to mitigate, it is necessary to assess beneficiary numbers for an average year. This

analysis has an added advantage over

analysis for any single year as it is notsubject to the at times significantfluctuations in individual year conditions

that may or may not be generallyrepresentative of the food secure status of

the assessed areas. It thus provides a soundindicator ofchronic food insecurity.

The reference year used for the LIUanalyses is not necessarily an average yearas defined mathematically by, for example,

crop production and market prices. Thesignificance of this difference is illustrated

in Figure 1 below. Clearly, fewer peoplewould be classified as chronically food

insecure if an average year is better than thereference year (case (A) in the figure), while

more people would be classified as foodinsecure if an average year is worse than the

reference year (case (B) in the figure). The same qualification applies to the analysis results ofany one individual year assessed as well.

a) Data sources used to assess an average year

For the purposes of this analysis, average crop production and market price levels have been

calculated for the last seven years, from the production years of 2000/01 through 2006/077. These

6Please see Annex B for further detail on the survival and livelihoods protection thresholds.

7A production year for the purposes of HEA baselines and analysis begins for agricultural areas with the start of

the harvest. The production year for Kalu (Amhara) and Endamehoni (Tigray) is therefore from September

through August; in Deder (Oromiya) from November through October; and in Boricha (SNNPR) from July

through June.

Figure 1. Percentage of the Population Facing

a Deficit in Different Types of Year

(A)An average year that is betterthan the

reference year

(B)An average year that is worse than thereference year


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data on average year conditions have then been compared to reference year crop and market priceconditions. Average year total income is thus projected from the reference year baseline data.

For this analysis, CSA data at zonal level was used to calculate average year crop productionlevels, and DPPA prices at woreda market level were used to calculate average year market price

data8. Averages were calculated for those crops and commodities that are identified as key

parameters of household livelihoods in the reference year: sources of food or income thatcontribute significantly to total food and income access, so that a reduction in access to that onesource may have a significant effect on total access.

9

b) Timeframe chosen for analysis

Average crop production and market price levels were calculated over the last seven years:

2000/01 through 2006/07. A seven-year average was chosen rather than a five-year average because the last several years have seen relatively good crop production levels. Extending the

average earlier than these years of higher production safeguards, to some extent, against thepossibility that recent years of good production will not be the norm in future years. Using the

five-year average would likely underestimate the population in need of assistance in the latter

case. A seven-year average was chosen over a ten-year average because the methodology for CSAcrop production data collection for years prior to 2000/01 was different than that used after thatpoint. The 2000/01 thorough 2006/07 timeframe was therefore determined to be the most sensible

and analytically sound timeframe.

c) Discussion of average year data

Time series graphs for crop production and market price data from 2000/01 to 2006/07 arepresented in the accompanying Excel workbook file,PSNP_Variable Levels of Support_Data.xls,

in the worksheet, crop & prices time series. Reported crop production trends across zones andyears are relatively consistent, despite some variation among individual years within each region.

Crop production levels generally rise as time goes on, notwithstanding significant increases in

cereals production in 2003/04, followed by fluctuation among higher levels of production through2006/07, as pictured for Kalu Woreda in Amhara. Tigray is the exception to this: after asignificant increase in reported production levels in 2003/04, all crop production returns to

roughly around 2000/01 levels.

8DPPA data on market prices, rather than CSA data, were chosen for the analysis for two reasons. First, DPPA

data is more comprehensive: it is available at woreda market level for all of the pilot case woredas. CSA market

price data is only available at a handful of markets in each region. CSA market price data may therefore not

most accurately represent actual market prices encountered by households in any given year and are likely tovary from the prices encountered at local woreda markets used by the majority of households due to

differences in transportation and transaction costs and related market access factors. Second, DPPA data is

readily available, whereas obtaining CSA data would have cost Birr 15,000 for each year of data requested.

Should the World Bank want an assessment of average year conditions using CSA market price data, this can be

considered in the future.

9In mathematical terms, a key parameter is defined as a source of income or food that either a) provides more

than 5% of income or food for at least 2 wealth groups; or b) provides more than twice that level (i.e. 10%) of

income or food for one wealth group.


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Figure 2: Total Crop Production Levels in East Hararghe Zone, Oromiya,

2000/01 2006/07

Note: bars in red indicate reference year and average year.

Market prices were averaged for the months of peak purchase (in the case of staple foods) and

peak sale as commodities following the harvest. Market prices for all regions have increasedsignificantly since 2000/01. For instance, in Kalu woreda, staple maize prices in the peakpurchase month of August in 2006/07 were roughly 250% of 2000/01 prices. Kombolcha market

prices in 2006/07 for maize sold after the meher harvest in November were 225% of 2000/01prices (rising from Birr 80 to 180). These trends are similar across regions. However, as for crop

production levels, average prices remain somewhat similar to levels in the reference year (2005/06for Kalu, below), when information on livelihoods strategies was collected. This is shown through

a comparison of these levels in the time series graphs in the crops & price time series worksheet,where reference year and average year prices are highlighted in red

10.

Figure 3: Staple Food Prices in Kalu woreda, 2000/01 2006/07

Note: bars in red indicate reference year and average year.

10Crop production and market price data on cash crops such as coffee and chat, as well as root crops such as

sweet and Irish potatoes important in SNNPR and Oromiya only began to be collected in 2003/04. Averages

of production and price levels may therefore reflect the higher production and price levels likely experienced in

the latter years of the 2000/01 2006/07 timeframe.


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ii) Assessing Beneficiary Numbers for Scenario B: High Price Inflation

Scenario B was developed in order to determine the population requiring support under conditionsmore closely resembling current year conditions of high staple food price inflation (and average to

high crop production). It is intended to provide an option which can be used for the immediateneeds of the pilot program, one of whose aims includes determining whether, from an operational

standpoint, it is possible for woreda administrations to successfully administer variable levels ofPSNP support.

Because of atypical current year conditions, woreda administrations in the pilot woredas face highsupport needs due to high staple food prices which had risen to roughly 300 to 350% of 2005/06

levels by the 2008 Belg season. They also face specific expectations already generated byintroduction of the pilot programs into the four woredas, particularly with regard to the number of

beneficiaries that would be included in the program.

Populations requiring support under Scenario B have been assessed using the same analysis

methods outlined briefly above and in greater detail in Annex A. Scenario market price levelswere generated from DPPA woreda level market price data from the 2008 Belg. Price problem

specifications are shown below in Table 2. Scenario B data for crop production has been either a)averaged for 2000/01 through 2006/07, or b) taken at 2006/07 levels.

Table 2: Market Prices Scenario B

Woreda Reference year

Staple food

prices as % of

reference year

Commodity crop

prices as a % of

reference year

Livestock prices as

a % of reference

year

Kalu 2005/06 300% 120 140% 160 200%

Endamehoni 2005/06 350% 130 350% 120 140%

Deder 2006/07 300% 120 150% 100 150%

Boricha 2003/04 500% 150 300% 250 300%


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III. Beneficiary Analysis Results: Scenario A an Average Year

Figure 4 below which presents a comparison between HEA livelihoods protection beneficiariesrequiring greater than 1.5 months support and PSNP beneficiaries summarizes the overall

findings of the data analysis in four pilot woredas. It highlights one of the main findings of thedata analysis: in all of the pilot woreda cases, PSNP support covers above the levels of support

required to cover the livelihoods protection deficit11. This is discussed further in Section IV:Discussion of Data Analysis Results.

Figure 4: Summary: Livelihoods Protection Beneficiaries vs. PSNP Beneficiaries

Note: Bar chart data does not include l/hoods protection (LPT) beneficiaries with

deficit durations of less than 1.5 months.

Further woreda breakdowns are presented below. Comparisons of survival deficit beneficiaries

(which are generally low) with PSNP beneficiaries are presented in tabular form, while livelihoods protection and PSNP beneficiary comparisons are presented as bar graphs12

. Full interim dataresults presenting these figures broken down by livelihood zone can be found in PSNP_Variable

Levels of Support_Data.xls under each woreda tab.

11A total or livelihoods protection deficit represents the survival deficit, plus a largely cash-based livelihoods

protection deficit representing cash needed to support livelihoods in the medium to long term: e.g. cash for

agricultural inputs, livestock drugs, health care costs, education, etc. Thus the livelihoods protection deficit is

over and above the survival deficit.

12Note that deficit durations below 1.5 months (or 0 months support associated with this duration range, as

indicated in Table 1 above) are also included in these results, unlike in Figure 4 above.


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A. Kalu Woreda, Amhara (Scenario A)

Table 3: Survival Deficit: Number of Beneficiaries Requiring

Different Durations of Assistance in an Average Year

District scenario Deficit

Duration

Total

Population

% Woreda

Population

Kalu - avg year


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C. Deder Woreda, Oromiya (Scenario A)

Table 6: Survival Deficit: Number of Beneficiaries Requiring Different

Durations of Assistance in an Average Year


Duration

Total

Population

% Woreda

Population

Deder - avg year


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IV. Discussion of Data Analysis Results Scenario A

A. PSNP Support Covers the Survival and Livelihoods Protection Deficits

An important conclusion from the data analysis is that in all of the pilot woredas, PSNP support

easily covers the survival deficit or absolute deficit of food of populations with this type of

deficit in an average year

13

. Indeed, only in Boricha woreda in SNNPR does a population face asurvival deficit of 3 months or greater and this population is under 5% of total woredapopulation (which represents half of the total population covered by PSNP support in Boricha).

By contrast, PSNP support is intended to cover 6 months of support for its beneficiaries.14

It is the livelihoods protection threshold that more closely corresponds with the current PSNP

beneficiary numbers and extent of support15. As indicated above in the summary graph (Figure 4above), PSNP support is sufficient to protect assets, and, although it is not intended to, it may be

able to build assets on a limited scale. This is particularly true in cases where current actual PSNPsupport covers far greater than the livelihoods protection needs of households, as in the cases of

Endamehoni and Deder woredas.

B. Strengthening Targeting of PSNP Resources Among WoredasThe data analysis results for Endamehoni in particular should be qualified by its relative wealth

within the region. Endamehoni woreda has a significant poor household population with total(food + cash) income

16levels that are above the regional average. In this woreda, over 70% of the

population falls under one of the wealthiest livelihood zones in the region: Alaje Ofla Highland

(AOH) Livelihood Zone, a generally food-secure, mixed farming zone, with diverse croppingoptions and good opportunities for casual and migratory labour for poorer households. As shown

in Figure 8 below, reference year total income (food + cash) for poor households in AOHLivelihood Zone is roughly 25% higher than the total income regional average of poor households

receiving PSNP support in Tigray region. Reference year cash income for poor households inAOH Livelihood Zone is 150% that of the regional average for poor households.

13However, it is important to note that HEA analysis does not model behaviour. Malnutrition rates may still be

high in these cases as people may often reduce food intake rather than reduce expenditure levels in order to

protect their assets in the longer term.14

It is reported, however, that PSNP support is often diluted at woreda level, so that a greater number of

beneficiaries receive a smaller amount of support.

15 The conceptual basis of the livelihoods protection threshold also more closely corresponds with the objectivesof the PSNP as this threshold aims to account not only for minimum short-term survival needs, but also for

those resources needed to maintain current levels of assets and wealth so that households do not fall deeper

into poverty, and incur increasingly larger deficits with each successive year.16

Total (food + cash) income equals annual household food and cash income together. Food and cash income

are converted into a common unit: either food energy requirements (kcals) or cash (Birr) usually the former. It

is used to measure the extent to which households are able to meet both minimum annual food needs and

livelihoods protection expenses for each wealth group. Total (food+cash) income is different than total cash

income, which is simply total Birr earned by a household from e.g. crop and livestock sales, migratory labour,

etc.


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Figure 9: Total Income (Food + Cash) Comparisons Across Livelihood

Zones and Regions17

This is a major reason why the populations facing both survival and livelihoods protection deficitsin Endamehoni are so low in the analysis results presented above, where 0% of the woredapopulation faces either a survival or livelihoods protection deficit over 1.5 months. Only 6% of

the woreda population faces a livelihoods protection deficit between 0 and 1.5 months in duration.By contrast, PSNP support is extended to 28% of the woreda population, as indicated Figure 10

below. This finding suggests that Endamehoni does not appear to be typical of other woredas inthe region.

Figure 10: HEA Livelihoods Protection Beneficiaries Requiring Different

Durations of Support vs. PSNP Beneficiaries in Endamehoni Woreda, Tigray

Baseline data collection for Oromiya region is still ongoing, and therefore a regional averagecannot be presented and compared with income levels in Deder woreda. As shown in Figure 8

17Six livelihood zones in SNNPR and several in Oromiya regions are excluded from regional averages of total

(food + cash) income, as they are not included in emergency programming due to their food-secure status.


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above, reference year income levels for poor households in Boricha (SNNPR) and Kalu (Amhara)woredas are roughly comparable to the regional averages.

C. Strengthening Targeting of PSNP Resources Within Woredas

It is clear from the breakdowns of HEA livelihoods protection beneficiaries by livelihood zonethat some livelihood zones are worse off than others within the same woreda. PSNP targeting

could be improved within woredas by redirecting some PSNP support resources from PAs inlivelihood zones where support covers well above livelihoods protection requirements to PAs in

livelihood zones that are worse off within the woreda but that do not receive sufficient PSNPsupport to cover livelihoods protection requirements.

Looking at livelihood zone breakdowns of support indicates that a small reallocation of resourceswould be appropriate for all pilot woredas assessed in this analysis. In Kalu woreda, for example,

PSNP support covers far more than households need to meet livelihoods protection needs in PAsin Chefa Valley (CHV) Livelihood Zone, but PSNP support fails to cover all livelihoods

protection needs in PAs in South Wollo and Oromiya Eastern Lowland (SWS) Livelihood Zone.CHV and SWS populations make up roughly 40% of the total woreda population each so

reallocation of about 30 to 40% of resources from CHV to SWS would be both feasible andappropriate.

Figure 11: Livelihoods Protection vs. PSNP Beneficiaries in Kalu

Woreda Livelihood Zones as a % of Total Woreda Population

This is also the case in Boricha woreda, SNNPR. Currently, PSNP support covers four times theneeds of the population in Sidama Coffee (SCO) Livelihood Zone, supporting 11,000 people,

compared to the 3,200 people who face a livelihoods protection deficit of 3 months. However,roughly 2,000 households in PAs falling under Sidama Maize Belt (SMB) Livelihood Zone

require support but do not currently receive it. Indeed, it is only in SMB Livelihood Zone wherepeople face 6 months of livelihoods protection deficit roughly 2,000 people in an average year).


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Figure 12: Livelihoods Protection vs. PSNP Beneficiaries in Boricha

Woreda Livelihood Zones as a % of Total Woreda Population

Please see the summary graphs inPSNP_Variable Levels of Support_Data.xls for livelihood zonebreakdowns for the other pilot woredas, where similar resource allocation issues also exist.


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IV. Beneficiary Analysis Results: Scenario B Pilot Figures

Data results for Scenario B, which identifies the population facing different durations oflivelihoods protection deficit under conditions resembling current year crop production and market

price conditions (e.g. high food price inflation), are presented below. An adjusted populationfigure is indicated in the highlighted columns in Tables 8 through 12, projected from Scenario B

beneficiary numbers in order to match the current number of PSNP beneficiaries receiving supportin the pilot woredas, as requested by woreda officials for the purpose of the pilot.

A. Kalu Woreda, Amhara (Scenario B)

Table 8: Total (Survival + L/hoods Protection) Deficit: Number Beneficiaries Requiring Different

Durations of Assistance


Duration

Total

Population

% Woreda

Population

Adjusted Population

for WB Pilot*

% Woreda

Population

Kalu Scenario B:


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C. Deder Woreda, Oromiya (Scenario B)

Table 10: Total (Survival + L/hoods Protection) Deficit: Number Beneficiaries Requiring Different

Durations of Assistance


Duration

Total

Population

% Woreda

Population

Adjusted Population

for WB Pilot*

% Woreda

PopulationDeder Scenario B:


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Figure 13: Percent Population Facing Different Levels of Deficit (-) or

Surplus (+) in SWS Livelihood Zone (Scenario B)

Household assets are calculated at these deficit cut-offs and presented in Table 12 below.

Table 12: Household Asset Levels at Deficit Level Cut-offs in SWS Livelihood Zone, Kalu woreda (Scenario B)

South Wollo & Oromiya Eastern

Lowland (SWS) Livelihood Zone Household Characteristics/ Asset Levels at Deficit Duration Cut-off

DeficitDuration

(months)

No.Beneficiaries

(LP t/hold) Cut-off

HHsize

landowned

(ha)*

landcultivated

(ha) oxen cattle

sheep/goats donkeys hens

eucalyp-tus

trees

< 1.5 mo. 9,327 1.5 mo. 6.9 0.4 0.2 0 3 4 0 6 241.5 - 4.5 mo. 18,654 4.5 mo. 6.0 0.3 0.2 0 2 2 0 5 15

4.5 - 7.5 mo. 27,410 7.5 mo. 4.7 0.1 0.1 0 0 0 0 4 2

total 55,391

*1 hectare = 4 timad

Household asset levels at cut-offs for all other livelihood zones in the pilot woredas for Scenario Bare presented in the Cut-off Asset Levels tab inPSNP_Variable Levels of Support_Data.xls.

4.5 mo.

deficitcut-offpoint at28

thpopn

percentile

1.5 mo. deficit

cut-off point at48

thpopn

percentile


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V. Packaging the Data: Providing Woredas with Appropriate Tools to

Implement Variable Levels of PSNP Support

With some further time and resources, tools using LIU databases could be developed to provide

the information tools to facilitate allocation of variable levels of PSNP support to woredapopulations.

Tools would provide the type of information presented above that would enable officials to

identify:

e) Where PSNP resources should be targeted within regions e.g. woredas with high food-insecure populations;

f) Where PSNP resources should be targeted within woredas e.g. livelihood zones withproportions of population falling under the livelihoods protection deficit;

g) Number of beneficiaries who should receive each level of support (e.g. 3 months, 6months, etc), disaggregated by wealth group and within each woreda livelihood zone;

h) Household asset levels at each support level cut-off to facilitate identification ofhouseholds at community level who should receive different levels of support.

Livelihoods Impact Analysis Spreadsheets are used by DPPA and Ministry of Agriculture officials

at federal and regional levels for early warning purposes and assessment of emergency needsduring seasonal assessments. Information and therefore exists that could link with these tools to

update food security contexts. Adequate capacity may also exist to use the tools, particularly atregional level, where officials have participated in LIU data collection, spreadsheet analysis,

seasonal assessment, and other trainings.


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VI. Conclusions

Using LIU data to identify potential PSNP beneficiary numbers in each woreda may present an

opportunity to more effectively target PSNP support to the households most in need. It alsoallows for more precise targeting of support in terms of duration of assistance. It is also possiblethat if, after allocating resources to those most in need (i.e. to those households facing livelihoods

protection deficits of different durations) within woredas, significant additional resources remain,resources could also be allocated to livelihood zones and woredas facing survival and/or

livelihoods protection deficits but not currently receiving PSNP support. These areas could beidentified by LIU data analysis or a form of the data tools discussed above.


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Annex A: How the LIU Baseline Data Can Be Used to Estimate the Percentage

of the Population Facing Different Levels of Deficit

The LIU database contains information on total income (food plus cash) for four wealth groups

(very poor, poor, middle and better-off) in each livelihood zone in a defined recent reference year.When compared to a defined income threshold, this can be used to estimate the deficit faced by

each wealth group. The LIU uses two different thresholds, the survival threshold and thelivelihoods protection threshold (see Figure A for an explanation of these). The proposed analyses

will be run for each type of deficit the survival deficit and the livelihoods protection deficit. Theprocedure for estimating the percentage of the population facing different levels of deficit is

described below and in the accompanying figures.

Step 1: Plot the Data for Each Wealth Group

Against the Population Percentile. The first stepis to convert the data by wealth group into a

continuous line showing the relationship between

level of wealth and level of deficit/surplus. Forthis analysis, the x axis represents the population percentile. We can think of this as

taking 100 people, and lining them up from thepoorest person on the far left of the x axis to the

richest person on the far right. The y axisrepresents months of deficit or surplus. The

figure illustrates how the first point, for very poorhouseholds, is plotted. Suppose the very poor

make up 8% of the population and have anaverage deficit of 5.1 months. We can convert

this into a single point on the graph by assumingthat the typical or middle very poor household (i.e. the 4th

household out of 100) has a deficit

equal to the average for the wealth group, i.e. 5.1months.

Step 2: Establish the Relationship between

Wealth and Level of Deficit/Surplus. Once thedata for all four wealth groups has been plotted, itis then possible to connect the 4 points and

establish the relationship between wealth andlevel of deficit/surplus. Preliminary analyses for a

number of livelihood zones indicate that therelationship is roughly linear, as illustrated in the

figure.

Step 3: Estimate the % of the Population Facing

a Deficit. This can be done by determining the

point on the x axis at which the line crosses zeroon the y axis. In the example in the figure, an estimated 52% of the population faces a deficit.

Step 1: Plot the Data for Each Wealth Group

Against the Population Percentile

Step 2: Establish the Relationship between

Wealth and Level of Deficit/Surplus


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Step 3: Estimate the % of the Population Facing a

Deficit

Step 4: Estimate the % of the Population Facing

Different Levels of Deficit


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Annex B: HEA Survival and Livelihoods Protection Deficits

Source: 2007. The Practitioners Guide to the Household Economy Approach. FEG Consulting, Save the

Children-UK and Regional hunger and Vulnerability Programme.

Figure A: Comparison of Projected Income against Two Clearly Defined ThresholdsProjected total income iscompared against twothresholds defined on thebasis of local patterns ofexpenditure.

The Survival Threshold

represents the total incomerequired to cover:

a) 100% of minimum foodenergy needs (2100

kcals per person), plusb) the costs associated

with food preparationand consumption (i.e.salt, soap, keroseneand/or firewood forcooking and basiclighting), plus

c) any expenditure on water for human consumption.

Note: Items included in categories b) and c) together make up the minimum non-food expenditure basket,represented by the brown bar in the expenditure graphic.

The Livelihoods Protection Thresholdrepresents the total income required to sustain locallivelihoods. This means total expenditure to:

a) ensure basic survival (see above), plusb) maintain access to basic services (e.g. routine medical and schooling expenses), plusc) sustain livelihoods in the medium to longer term (e.g. regular purchases of seeds, fertilizer,

veterinary drugs, etc.), plusd) achieve a minimum locally acceptable standard of living (e.g. purchase of basic clothing,

coffee/tea, etc.)

PSNP Variable Levels of Support_FINAL

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