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By: Heba El LaithyDina Armanious
Beneficiary Selection Process for UNHCR Multipurpose Cash Assistance (MPCA)
April 2019
Cover photo credit:Pedro Costa Gomes
Design Credit:UNHCR Egypt
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Contents
Executive Summary 3Introduction 4
1. Proposed Proxy Means Testing for UNHCR Beneficiary Selection Process 6
1.1. Proxy means testing and other targeting approaches 61.1.1. Individual/ household assessment 71.1.2. Categorical targeting 81.1.3. Self-selection 91.2. Developing a Proxy Mean Testing Formula (PMTF) for Refugees 101.2.1. Determining PMT Variables and Weights 101.3. Performance of Proxy Means Formula 141.3.1. Criteria for Measuring PMT Targeting Performance 141.3.2. Assessing PMT performance for Refugees in Egypt 161.4. Determining Eligibility 192. Key Elements for Sound Implementation Process 202.1. Data collection processes 202.2. Managing Unified Household Information Systems 242.3. Monitoring, verification, and fraud control 24
2.4.Determining Household Eligibility by Calculating Composite PMT Scores 25
2.5. Monitoring and Evaluation 262.6. Setting Graduation Framework 272.6.1. Maximum Time Limits 272.6.2. Defined Exit Thresholds 272.6.3. Gradual Benefits Reductions 27Annex A 29Acknowledgment 32References 32
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List of Tables
Table 1: Variables included in different models 13Table 2: Illustration of Type I and II errors 15Table 3: Performance indicators for different models 18Table 4: Relative Advantages of Different Data Collection Processes: Outreach
Survey vs. On-Demand Application Approaches 22
List of Figures
Figure 1: Scatter Diagram for ln per capita expenditure and its predicted values for different models
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Figure 2: Under-coverage and leakage according to different estimated models 17Figure 3: Key Phases Influences Targeting Accuracy for Transfer Programs 28
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The overall objective of this report is to refine the proposed targeting approach (the existing PMT model based on UNHCR’s household survey) for multi-purpose cash assistance, as well as recommendations on data elements to be integrated in grievance and redress mechanisms in light of inclusion and exclusion errors associated with the proposed PMT, and to ensure assistance is efficiently allocated to the most vulnerable.
Models based on UNHCR’s household survey (EVAR) and global refugee database are developed. However, this approach of targeting identifies vulnerable refugees and asylum-seekers and is validated in light of inclusion and exclusion errors, in addition to the comparison with the existing national targeting approaches. Accordingly, the overall objective of this report is to refine the proposed targeting approach (the existing PMT model based on UNHCR’s household survey) for multi-purpose cash assistance, as well as recommendations on data elements to be integrated in grievance and redress mechanisms in light of inclusion and exclusion errors associated with the proposed PMT, and to ensure assistance is efficiently allocated to the most vulnerable.
Proxy-means tests (PMT) were developed to generate a score for applicants based on fairly easy to observe characteristics of the household such as the location and quality of the dwelling, ownership of durable goods, demographic structure of the household, and the education of adults. Scores are composite index that reflect welfare levels.
Executive Summary
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In an effort to support the most vulnerable refugees and to avoid their resorting to harmful coping strategies, UNHCR provides a monthly unconditional cash grant ranging from EGP 600 to EGP 3,000 to the most vulnerable households. These cash transfers are provided to an average of 16,100 extremely vulnerable refugee households. However, due to funding levels, the monthly unconditional cash support to the most vulnerable households is limited to only 30% of the refugee population whereas previous household surveys and refugee consultations indicate that those in need are far much higher. Eligibility to this cash-based intervention has for most part been based on categorical targeting focusing on social groups and people with protection risks such as female headed households, persons with serious medical conditions or disabilities, older persons among other traditionally vulnerable groups.
However, since 2014 UNHCR has shifted targeting of food and cash assistance from a geographical and categorical basis to socio-economic criteria by understanding of economic vulnerability for the targeting of cash assistance. This is a relatively new approach in refugee contexts. To that end, a multi-sector household-level survey, under the auspices of the Egypt Vulnerability Assessment for Refugees (EVAR) was developed to collect data to inform targeting efforts. UNHCR Egypt seeks to develop proxy means test (PMT) formula as the primary modality to identify refugees and asylum seekers to be targeted for this cash-based intervention.
In 2018, UNHCR Egypt has adopted the proxy means test (PMT) as the primary modality to identify refugees and asylum seekers to be targeted for this cash-based intervention. A model based on UNHCR’s household survey (EVAR) and global refugee database has been developed. However, this approach of targeting and identifying vulnerable refugees and asylum-seekers has to be reviewed and validated in light of inclusion and exclusion errors, in addition to the comparison with the existing national targeting approaches. Accordingly, the overall objective of this report is to refine the proposed targeting approach (the existing PMT model based on UNHCR’s household survey) for multi-purpose cash assistance, as well as recommendations on data elements to be integrated in grievance and redress mechanisms in light of inclusion and exclusion errors associated with the proposed PMT, and to ensure assistance is efficiently allocated to the most vulnerable.
Proxy-means Tests for Targeting
Proxy-means tests (PMT) were developed to generate a score for applicants based on easy to observe characteristics of the household such as the location and quality of the dwelling, ownership of durable goods, demographic structure of the household, and the education of adults. Scores are composite index that reflect welfare levels. Three models for PMT were developed, Model 1 with full information on location, housing characteristics, head of household and members characteristics performed better than other models with respect to under-coverage rates.
Introduction
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Steps for Implementation
1. Data Collection
The report provides several approaches to collect data; home visits or on demand interviews. Home visit seeks to verify visible living conditions of the family, particularly housing quality, public services and presence of durable goods.
UNHCR can also rely on ProGres data (the Profile Registration Global System) for targeting purposes. Thus, there are needs for revising ProGres to allow for the regular collection of vulnerability indicators. If UNHCR depends on ProGres, refugees’ information need to be verified after 6 months of the initial registration where data (such as place of residence and occupation status) should be updated and include additional vulnerability indicators (such as housing conditions, exposure to risks and transfers received). Refugees are required to approach the office for updating data every 18 months, where their PMT scores are revised and so as their eligibility status. Refugees have to report of any changes of their data at any time.
It is also recommended that a blanket approach to cash assistance is applied during the first 6 months of refugees’ initial registration. Refugees should be aware that payments are provided as emergency aid but after 6 months, their vulnerability status is reviewed and other eligibility criteria are to be applied. It should be noted that duration of asylum is taken into consideration while assessing refugees’ vulnerability scores and hence their eligibility.
2. Identifying Eligibility
Once data are collected, they are entered into a unified household registry and cleaned. Household PMT composite indices (scores) are then constructed using the data collected on the variables in the index and the pre-determined weights. This construction is usually an automated process, having been programmed into the registry software. These household PMT scores are then compared to previously-established eligibility cut-offs (thresholds) for particular social programs. These cut-offs can be specific score levels or ranges of scores.
3. Establishing Appeals Framework
To mitigate the exposure to risk caused by exclusion and to acknowledge potential inaccurate predictions, Appeals mechanisms have to be established to allow refugees to request a re-evaluation of their eligibility for assistance after exclusion.
4. Monitoring and Evaluation
It is essential to carry out a continuous and permanent monitoring, and a constant effort at correcting possible deviations and abuses, as well as to improve its operation and adapt it to changing or unexpected situations.
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Targeting is a means of increasing program efficiency by increasing the benefit that the poor can get within a fixed program budget. The rationale for targeting is to ensure that limited program resources primarily reach the poor and that the poor, or sub-groups of the poor, are not excluded. The objectives of targeting are fully consistent with “universal coverage of the poor,” which is emerging as a goal for some large programs. Most cash transfers programs require some sort of mechanism for screening households to determine eligibility, since the immediate benefits (transfers) are largely “private” (as opposed to “public”) goods.
Scarce resources have encouraged efforts to concentrate resources on “target groups” of poor households or individuals. This will achieve the maximum impact from a given poverty-alleviation budget or achieve a given impact at the least budgetary cost. Although targeting has obvious benefits, numerous methods exist for directing resources to a particular group.
There is no clearly preferred method for all types of programs or all country contexts. The motivation for targeting arises from the following three features of the policy environment:
• Objective: the desire to maximize the reduction in poverty or, more generally, the increase in social welfare
• Budget constraint: a limited poverty alleviation budget
• Opportunity cost: the tradeoff between numbers of beneficiaries covered by the intervention and the level of transfers.
1. Proposed Proxy Means Testing for
UNHCR Beneficiary Selection Process
1.1. Proxy Means Testing and Other Targeting Approaches
Several targeting methods are available for several program types, both the choice of targeting method and its thoughtful implementation require knowing a great deal about how each method works in general and variations on how it can work in particular circumstances or variants of implementation.
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A variety of targeting instruments for social programs, including household (or individual) assessment mechanisms, broad categorical eligibility, or self-targeting could be applied. Many programs adopt a combination of these mechanisms. As a general rule, in urban areas targeting methods that focus on individual households are likely to be more effective (proxy means tests, self-targeting, etc.). Whereas providing general public services and programs would be more affective in areas with high poverty rates. Methods of targeting could be classified into three broad forms:
1. Individual/ household assessment,
2. Categorical targeting, and
3. Self-selection
1.1.1. Individual/Household Assessment
Is a method under which eligibility is directly assessed on an individual basis. In a verified means test, (nearly) complete information is obtained on a household’s income and/or wealth and compared to other sources of information such as pay stubs, or income and property tax records. This requires the existence of such verifiable records in the target population, as well as the administrative capacity to process this information, and to continually update it, in a timely fashion.
Absent the capacity for a verified means test, other individual assessment mechanisms are used. For example, simple means tests, with no independent verification of income, are not uncommon. A visit to the household may help to verify in a qualitative way that visible standards of living (which reflect income or wealth) are more or less consistent with the figures reported.
Proxy-means tests involve generating a score for applicants based on fairly easy to observe characteristics of the household such as the location and quality of the dwelling, ownership of durable goods, demographic structure of the household, and the education of adult members. The indicators used in calculating this score and their weights are derived from statistical analysis of data from detailed household surveys. An increasingly popular approach to individual assessment has been to decentralize the selection process to local communities so that a group of community members or a community leader whose principal functions in the community are not related to the transfer program will decide who in the community should benefit and who should not. Detailed description of Proxy means test is outlined below.
Proxy Means Test: Rationale and Methodology
In principle, conducting a means test that correctly measures the earnings of a household is the best way to determine eligibility when the poor are the target group. However, income is always considered an imperfect measure of welfare in developing countries, since it is unlikely to measure accurately imputed value of own-produced goods, gifts and transfers, or owner-occupied housing. Incomes of the poor in developing countries are also often subject to high volatility due to factors ranging from seasonality of agriculture and unstable nature of employment in the informal sector.
Given the administrative difficulties associated with sophisticated means tests and the inaccuracy of simple means tests, the idea of using proxy means tests that avoid the problems involved in relying on reported income is appealing. The idea of proxy means
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test formula (PMT) is to find a weighted combination of “proxy” variables/indicators that together identify or predict whether a household is poor or not.
Proxy means test uses information on household or individual characteristics correlated with welfare levels in a formal algorithm to proxy household income or welfare. These instruments are selected based on their ability to predict welfare as measured by, for example, per capita consumption expenditure. The obvious advantage of proxy means testing is that information is collected on items which are much easier to measure and verify, such as demographic data, characteristics of dwelling units and ownership of durable assets, etc. These variables should be ones which are known to correlate with poverty in the country, and ideally, which are easy to measure and thus require little administrative cost to verify.
The advantage of PMT is that it allows fairly good individual-level targeting of program benefits using a relatively small amount of information, without having to collect information on incomes or expenditures that may be unreliable. The term proxy means test denotes a system in which scores for applicant households are generated from characteristics that are fairly easy to observe—for example, the location and quality of the family’s dwelling, the durable goods they own, the demographic structure of the household, levels of education, and adult occupation in some cases. The indicators and weights to calculate individual scores are derived from deeper statistical analysis based on household surveys, usually regression analysis or principal components. Hence, development of a PMT requires nationally representative household data that has information on incomes, expenditures, and a variety of household and socioeconomic characteristics.
1.1.2. Categorical Targeting
Also referred to as statistical targeting, tagging or group targeting - involves defining eligibility in terms of individual or household characteristics that are considered to be easy to observe, hard to falsely manipulate, and correlated with poverty. Categorical targeting includes geographic targeting and demographic targeting. Eligibility for benefits is determined in Geographic targeting, at least partly, by location of residence. This method uses existing information such as surveys of basic needs or poverty maps. While eligibility is determined in demographic targeting by age, gender, ethnicity, land ownership and household demographic composition. Geographic targeting is often used and often in tandem with other methods. Poverty Maps are essential poverty geographical targeting tools. Poverty maps are generally defined as the spatial representation and analysis of indicators of wellbeing and poverty (Davis 2002). Simply described, a poverty map is a visual representation of where poverty is concentrated. Maps encourage visual comparison and make it easy to look for spatial trends, clusters, or other patterns. It provides a clear criterion for identifying the target population and avoids the informational constraints that impede most other targeted programs. Geographic targeting has relatively little influence on household behavior since it is difficult and costly to change the place of residence.
The instruments of geographically targeted programs can include not only direct income transfers to the target population, but also a variety of other measures aimed at increasing the income of the population. Geographic targeting can thus provide guidelines for both the allocation of benefits under a country’s welfare program, and the allocation of resources under the country’s development program. For this reason,
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poverty maps are particularly used by social funds around the world. Geographically targeted interventions or local area development particularly depends on poverty maps as an effective targeting tool.
1.1.3. Self-selection
With some interventions, although eligibility is universal the design intentionally involves dimensions that are thought to encourage the poorest to use the program and the non-poor not to do so. This is accomplished by recognizing differences in the private participation costs between poor and non-poor households. For example, this may involve: (a) the use of low wages on public works schemes so that only those with a low opportunity cost of time due to low wages or limited hours of employment will present themselves for jobs; (b) the restriction of transfers to take place at certain times with a requirement to queue; or (c) the location of points of service delivery in areas where the poor are highly concentrated so that the non-poor have higher (private and social) costs of access. An alternative form of self-selection is found in social fund-type interventions where communities apply for program funds. Note that universal food subsidies (with or without quantity rationing) can be viewed as a form of self-selection since they are universally available and households receive benefits by deciding to consume the commodity. In practice households can often determine not just whether or not to participate, but also the intensity of their participation.
The more income elastic are expenditures on these items the more effective is the targeting. For example, food transfers often involve commodities with “inferior” characteristics (e.g. low-quality wheat or rice) and households often substitute away from such expenditures as incomes increase.
Since 2014 UNHCR has shifted targeting of food and cash assistance from a categorical basis to socio-economic criteria by understanding of economic vulnerability for the targeting of cash assistance.
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1.2. Developing a Proxy Mean Testing Formula (PMTF) for Refugees
Proxy-means test (PMT) was developed for refugees in Egypt for the purposes of deriving the most accurate formula that identifies the poor and those with protection risks. Different models were estimated and assessed using simulation. It does a good job of identifying the poor and performs very well when compared other countries and to the national PMT used for Takaful and Karama cash transfer program of MOSS. The advantage of PMT is that it allows fairly good individual-level targeting of program benefits using a relatively small amount of information, without having to collect information on incomes or expenditures that may be unreliable. Development of a PMT requires representative household data that has information on incomes, expenditures, and a variety of household and socioeconomic characteristics that are highly correlated with poverty or consumption levels. EVAR data set outlined above was used to develop Proxy-means testing formula for refugees.
1.2.1. Determining PMT Variables and Weights
As in all living standard and poverty assessment in Egypt, per capita consumption is used as welfare measure. To predict welfare, the consumption variable is regressed, using OLS method4, on different sets of explanatory variables. The case for using OLS as the model for predicting welfare is driven primarily by convenience and ease of interpretation. However, owing to the obvious endogeneity of some explanatory variables, no causal interpretation should be assigned to the coefficients. The sole purpose here is to determine their predictive power. The econometric models’ methodology uses predicted consumption as a proxy for refugee welfare, and hence can be used for identifying eligibility for targeting.
We followed a systematic approach. First, household head variables at the individual level (like level of education) were aggregated at the household level by choosing the values from the household head. Second, to reflect individual characteristic within each household, variables representing counts per household (like number of children in school, number of adults, disabled …etc) were transformed into household variables by dividing for the household size. Third, dummy (0/1) variables were created for the remaining categorical variables (place of residence, experienced risks, etc.).
Welfare indicator is per capita monthly consumption. Consumption includes out of pocket spending as well as food vouchers. Monthly consumption was derived by converting all consumption items into monthly consumption and adding them up to
4 OLS estimate the relationship between welfare variable (Y) and explanatory variables (X) by assigning coefficient (weight) to each explanatory variable. It minimizes the sum of squares of the differences between the observed scores of Y (the values of Y from the sample of data) with the expected values of Y (the values of Y predicted by the regression equation).
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obtain household monthly consumption. Expenditure that was not consumed (such as in-kind transfers to other households) is not included.
There are several conceptual and empirical reasons to use of expenditure/consumption, as opposed to income, as the basis for the welfare indicator in developing countries like Egypt, since;
1. All income is not consumed, nor was all consumption financed out of income, consumption is better reflecting what households can command;
2. Incomes, specially for the poor, may be seasonal in nature, therefore, expenditures are a better indicator of longer run living standards than current income, since consumption tends to smooth variability and fluctuations in income streams;
3. A practical problem of using income to indicate welfare lies in the measurement of incomes of people who operate their own business where records of family businesses are often not kept and finally;
4. Survey respondents may be more willing to reveal their consumption patterns rather than their income. (Source: Hentshcel and Lanjouw, 1996.
Selection of Variables
To predict welfare as measured by per capita consumption takes into account two separate criteria are used: correlation between the welfare measure and the predictor, which determines accuracy of the prediction, and verifiability of the predictor, i.e., variable should be easy to be verified. The types of predictors used are highly correlated with poverty. Choice of proxy variables (predictors) must balance between 3 criteria: able to identify the poor with some accuracy; easily observable and measurable; cannot be manipulated easily by household.
We include variables that reflect household welfare level as well as its vulnerability. Using variables that reflect vulnerability for targeting cash assistance is justified from a UNHCR perspective due to the very nature of the refugee population. Due to difficulties in accessing protection and assistance, vulnerable refugees are more likely to revert to negative or harmful coping strategies such as removing children from school, engaging in child labor, early marriage, and other practices that impact family and individual welfare. These variables can be grouped as:
• Location variables are obviously the most easily verifiable; we used governorate and district dummy variables as predictors for location. Including districts and governorates dummies may entail redundancy, but we believe that governorates’ impact should be addressed as location variables as some households do not reside in the specified districts.
• Strata: Syrians, Arabic and non-Arabic speakers of other nationalities.
• Housing characteristics: crowdedness of the house, House type and type of occupancy dummies may also be easily verified by a social worker visiting the home.
• Household demographic and socio-economic characteristics such as duration of asylum, the number of members and dependents; and age and gender of household head and his/her marital status; education and
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employment characteristics of the household head and of other household members. We include age of the head and squared age as we believe that per capita consumption increases with the age of the head till certain age and then it decreases. Socio-economic characteristics are less easy to verify. However, it is generally felt that this information, is not overly difficult to verify.
• Protection risks facing household members: such as percentage of children exposed to risks, early marriage, disabilities.
• Types of transfers received: Dummy variables indicating whether the household received cash or in-kind transfers by World Food Program, Caritas, these variables can be verified through documents of UNHCR and partner agencies databases.
All the above sets of variables are used in constructing PMT for Egyptian cash transfers programs (Takaful and Karama) except the risk set and strata. However, we believe that risks experienced by refugees’ households affect their welfare. On the other hand, PMT for Egypt includes variables on housing conditions and ownership of some durable goods and assets which have usually good prediction power, but unfortunately the EVAR baseline survey does not provide information on them.
We developed three PMT models. Ln per capita consumption is used in all models as the dependent variables, while some variables were excluded in some models.
1. Model 1 includes all relevant variables (that are highly correlated with poverty) and that are in EVAR baseline survey,
2. Model 2 includes all variables as in model 1 except districts’ variables although they may have high prediction power. Households move easily from one district to another especially in urban areas, while its welfare level may not change.
3. We also explored the possibility of using the registration data base (proGres data) for targeting eligible families for cash transfers. We estimated the regression model using only variables in proGres data as explanatory variables. In model 3, registration database includes all variables except those of housing conditions. If proGres database is used, there will be no need to call for families to apply or collect data on them.
Moreover, admitting that refugees face similar challenges as Egyptian communities with more specific and protections needs of refugee boys, girls and parents, including unaccompanied and separated children and other children at risk, another PMT model another PMT model (Model 4) was estimated using the Household, Income, Expenditure and Consumption Survey, 2015 (HIECS). This Survey has larger size (24 thousands of households) and it is nationally represented at the governorate level. The survey demonstrates poverty and its correlates in Egyptian community. Model 4 includes common variables between EVAR and HIECS; the proposed PMT can identify the poor identified in HIECS 2015 and provide errors of inclusion and exclusion. The aim of this model is to cross check the predicted power of variables that are common in EVAR data and HIECS. Table 1 demonstrates differences in variables included in each model.
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Table 1: Variables included in different models
Model 1 Model 2 Model 3HIECS modelModel 4
LocationAll governorates and districts
Governorates onlyAll governorates and districts
Governorates only
Strata All Strata All Strata All Strata No Strata variable
Housing Characteristics
Crowdedness, House type and type of occupancy
Crowdedness, House type and type of occupancy
No housing characteristics
Crowdedness, House type and type of occupancy
Household head characteristics
Age, marital status, education, and employment
Age, marital status, education, and employment
Age, marital status, and education
Age, marital status, education, and employment
Members’ Characteristics
Household size, ratio of household members by:1) age and sex, 2) education,3) Employment
Household size, ratio of household members by: 1) age and sex, 2) education,3) employment
Household size, ratio of household members by: 1) age and sex, 2) education
Household size, ratio of household members by: 1) age and sex, 2) education,3) employment
Risks All risks All risks All risks No risk variables
Transfers All types of transfersAll types of transfers
All types of transfers All types of transfers
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1.3.1. Criteria for Measuring PMT Targeting Performance
Different specifications of the model and different samples of the population yield different results and it is not always easy to say which specification is superior. However, a variety of tests can be conducted, when taken together, can be used to select one model over another.
We use four types of criteria to evaluate alternate options for the PMT;
1. The regression’s R² which is the proportion of the variation in consumption is explained by the regression model. Higher the R², the better are a particular set of variables in predicting welfare. It is desirable that the model maximizes it’s the explanatory power.
2. The rank correlation between actual and predicted welfare measure measures the similarity between ranks of the same households according to their actual consumption and the predicted consumption based on the model under consideration. The higher the rank correlation, the greater the accordance between the household’s ranks.
3. Inclusion and exclusion errors: in practice, program officials do not have perfect information about who is poor because this information is difficult, time consuming, and costly to collect. Thus, when basing program eligibility on PMT, they may mistakenly commit errors of inclusion (Leakage)—identifying non-poor persons as poor and therefore admitting them to the program, or errors of exclusion (Under-coverage) — identifying poor persons as not poor and thus denying them access to the program. Individuals are categorized in four groups: classified as “poor/non-poor” and they are correctly classified as “poor/non-poor; those are targeting successes. When the predicted welfare classifies a poor person as non-poor, a targeting error has occurred. This kind of error is called a false negative, a Type I error or an error of exclusion. When individuals are non-poor and are predicted as poor, these individuals are thus incorrectly identified as being eligible for program benefits. This kind of error is called a false positive, a Type II error or an error of inclusion, and leads to the “leakage” of program benefits, see Table 2 for illustration.
4. Targeting efficiency across distribution of welfare: the proportion of beneficiaries falling out the bottom (poorest) 40 or 30% o: welfare distribution (leakage). And the proportion of benefits (transfers) that do not go to the bottom (poorest) 40 or 30% of welfare distribution (under-coverage). It is preferred that a model has good incidence, i.e. most of the identified beneficiaries (based on their predicted consumption) belong to the bottom of the actual consumption (income) distribution, and relatively few, if any, from the top of the distribution.
1.3. Performance of Proxy Means Formula
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A common approach to evaluate the targeting performance of alternative transfer instruments is to compare under-coverage and leakage rates.
Under-coverage rate is the proportion of poor households that are not included in the program (errors of exclusion, e1/N1). Leakage rate is the proportion of those who are reached by the program who are classified as non-poor (errors of inclusion e2/M1).
In general actions taken to reduce one kind of error may cause the other to increase. The fact that both types of targeting errors will occur and are generally inversely linked means that policymakers must decide how well they can tolerate each. An error of inclusion wastes program resources (e.g., by leaving less for “poor” households or by increasing the budget required to have the same poverty impact) and thus makes the program inefficient. An error of exclusion leaves some poor without help and makes the program ineffective at reducing poverty. Both are undesirable and different policy makers may have different views about which are worse. In general, the higher the priority assigned to raising the welfare of the poor, the more important it is to eliminate under-coverage. Conversely, the higher the priority assigned to saving limited budget funds, the more important it is to eliminate leakage. Lowering leakage, besides being cost-efficient, can also be welfare increasing in the presence of a budget constraint – lower the leakage of benefits to ineligible individuals; higher would be the amount available for transfers to those who are eligible.
Table 2: Illustration of Type I and II errors
Target Group Non-target Group Total
Eligible: Predicted by PMTF
Targeting Success (s1) Type II error (e2) M1
Ineligible: Predicted by PMTF
Type I error (e1) Targeting Success (s2) M2
Total N1 N2 N
Source: Nayaran et al 2005.
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1.3.2. Assessing PMT Performance for Refugees in Egypt
We first simulate the UNHCR’s targeting process using the data at hand (EVAR) and poverty classifications. This provides a sense of whether our data can correctly estimate those who are intended to receive cash assistance. For simulation purposes, an obvious choice for this cutoff point is the poverty line estimated for HIECS 2017/18 at the region level. Poverty line for “Urban Governorates” region is EGP 773 per person per month, for “Urban Lower Egypt” region is EGP 711 and for other regions is EGP 727.
Poverty rates reached 39% and we are 95% confident that it ranges from 38% to 41%. We also simulate the models’ performance if UNHCR decided to target the poorest 30% of refugees’ population or the poorest 40%.
Table 3 below presents summary of simulated targeting performance; in terms of the four criteria mentioned in section 1.3.1 above.
R2 of all model performed in a reasonable value ranged from 0.6092 to 0.5887 and rank correlation ranged from 0.7572 to 0.7452. However, the real test of the simulation for our purposes is not the R2 but targeting accuracy.
Table 3 provides the results of an ex-ante evaluation of the levels of accuracy of the three models for predicting the needy and the non-needy as well as HIECS model. As indicated above, each model predicts a score for every household, these predicted welfare levels (scores) are used to assign individuals to eligible or ineligible groups, based on poverty line. Under-coverage and Leakage rates are then calculated, for each model.
Figure 1: Scatter Diagram for ln per capita expenditure and its predicted values for different models
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Model 1 performed better than other models with respect to most indicators, followed by model 2 then model 3. Under-coverage rates ranged from 16.8% for the first model to 20.7% for the third model as shown in Figure 1. Generally, under-coverage rates, and leakage are close for all PMT formula for Refugees, where differences between models do not exceed 2.1%. However, model 2 has the advantage of not including districts, so households’ scores can be evaluated with governorate effect only. Although the performance of model 3 is the worst, leakage and under-coverage rates are within an acceptable range. If model 3 were applied about 78.9% of the poor are targeted and will receive benefits, compared to 79.9% for model 1.
Out of eligible population, 80% of the poor, 9% of the near poor and 11% of the non-poor are covered, when we use PMT for model 1, the corresponding figures for model 2 are 79%, 9% and 12% and for model 3 are 79%, 10% and 11%, respectively. Thus, using registration database to target the poor may reduce cost for collecting data but some of the poor will be missed. Moreover, the performance of the three models have similar leakage rate. These rates are highly compared with targeting performance of the PMT model used for Takaful and Karama programs for cash assistance to poor Egyptians, provided by Ministry of Social Solidarity (MoSS), where under-coverage rate is 32.75% and leakage rate is 23.9%. Thus, if further alignment with the national targeting system can be considered, UNHCR can make use of the widespread and experienced social workers of MoSS and can use similar mechanisms for delivering cash transfers and verification procedures.
Figure 2: Under-coverage and leakage according to different estimated models.
No marked differences are observed between the three estimated models.
16.79% 16.74%
20.69%
32.75%
21.11% 21.11% 21.11%
23.90%
0.00%
5.00%
10.00%
15.00%
20.00%
25.00%
30.00%
35.00%
Model 1 Model 2 Model 3 PMT used by MoSS
Under-coverage rate Leakge rate
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The last four rows of Table 3 point to the targeting efficiency across distribution of welfare. They indicate the under-coverage and leakage rates if the intended targeted population is the bottom 30% or 40% of welfare distribution rather than the poverty line.
However, the proposed PMT models are not without limitations. The models do not take into account sustainability, they predict current expenditure. It does not take into consideration the economic reserves that a refugee household may have; so, this model is unable to predict future economic vulnerability.
To conclude, if it is feasible to collect data for targeting purposes, it is recommended to use model 1 as the PMT formula to target poor refugees for cash transfers. On the other hand, there is no need to collect more information when using ProGres database, but about 21% of the poor will be missed from receiving transfers compared to 17% under model 1.
There is tradeoff between cost and coverage. If decision makers overweight cost they should use ProGres database and PMT of model 3. If it is more important to reach larger coverage of the poor regardless of the cost, it is recommended to collect data using the form demonstrated in Annex A and use PMT of model 1.
Table 3: Performance indicators for different models
Model 1 Model 2 Model 3HIECS model
Adjusted R² 0.5876 0.5878 0.4583 0.5921
Rank correlation 74.61% 73.71% 64.42% 79.22%
Using poverty line
Under-coverage rate 16.79% 16.74% 20.69% 39.89%
Leakage Rate 21.11% 21.11% 21.11% 23.74%
% of the poor out of covered population 79.89 % 78.83 % 78.89 %
% of the near poor out of covered population 9.40 % 9.40 % 10.37 %
% of the non-poor out of covered population 10.71 % 11.81 % 10.74 %
% beneficiaries not in the bottom 30% of welfare distribution
23.66% 24.23% 25.62% 30.58%
% beneficiaries not in the bottom 40% of welfare distribution
19.32% 20.01% 22.98% 25.75%
% benefits (transfers) do not go to the bottom 30% of welfare distribution
23.79% 24.17% 25.58% 30.58%
% benefits (transfers) do not go to the bottom 40% of welfare distribution
23.79% 20.15% 23.02% 25.74%
19
The selection of the cutoff point is essentially a policy decision4, and not a technical decision. Eligibility cutoff point is the threshold of actual value of per capita consumption that classifies population into eligible and ineligible. The choice of cutoff points should balance between under-coverage errors; leakage errors; the extent to which the target groups benefit (percentage of targeted group covered) and the impact in terms of poverty reduction. The top priority for UNHCR is to ensure that the people most in need are helped – in other words, to minimize exclusion/errors (although it is important to consider both inclusion and exclusion). With limited budget, it is essential to focus on reducing inclusion errors while ensuring fairness and avoiding unnecessary expense.
4 A natural choice of cutoff point is the poverty line estimated for HIECS 2017/18 at the region level. Poverty line for “Urban Governorates” region is EGP 773 per person per month, for “Urban Lower Egypt” region is EGP 711 and for other regions is EGP 727. Poverty rates reached 39% and we are 95% confident that it ranges from 38% to 41%.
1.4. Determining Eligibility
UNHCR/Pedro Costa Gomes
20
Proxy-means tests involve screening households for eligibility using a composite score on a multi-dimensional index of observable characteristics (“proxies”) that are associated with poverty. The indicators used in calculating this composite score and their weights are generally derived from statistical analysis of household survey data. The design and implementation of PMT systems is illustrated by Figure 3 and usually involves three steps:
Step 1: Deriving PMT formula and test its performance. This step was discussed extensively in section 1 of the report.
Step 2: Collecting Data from Households
Step 3: Determining Household Eligibility by Calculating Composite PMT Scores.
In the following, steps two and three are discussed in more details.
2.1. Data Collection Processes: Approaches to Collecting Data
Once variables, weights and the composite PMT index are identified and constructed, a questionnaire can be formulated and data can be collected from households. Questionnaires are not long (usually ranging from 2-5 pages). Data can be collected via an interview by a trained survey interviewer. There are two methods to collect data; home visits or on demand interviews (see Table 4). Home visit seeks to verify visible living conditions of the family, particularly housing quality, public services and presence of durable goods. As discussed below, households can be interviewed on an on-demand basis (applications initiated by the households themselves).
The two different methods for data collection discussed below – the “home-visits approach” and the “on-demand application approach” each imply very different logistical structures, which may or may not be suited to the administrative capacity of UNHCR. The “home-visits approach,” for example, requires (usually infrequent) assembly of large teams of interviewers to either conduct outreach census-style interviews (home visits) or to conduct mass registration on dedicated “registration days” (e.g., in public locations such as mosques or schools with advance publicizing of the event). The “on-demand application approach” requires a more permanent structure of points of contact (usually
2. Key Elements for Sound Implementation
Process
21
an extensive network of local welfare offices). Second, the frequency of updates and recertification should be administratively realistic and tailored to the characteristics of the intended target population (as discussed below). Office-based interviews may be appropriate for registration and to discuss protection concerns but are not the most effective for socio-economic assessment which benefits from direct observation of living conditions, asset holdings, and the extended household. Home visits are not only an opportunity to collect information on income and expenditures but are also valued as an outreach opportunity.
An intermediate solution might be to collect the information in the office, and to make household visits and verifications for a sample of beneficiaries. Households that had given inaccurate information would then have to pay some sort of penalty. To encourage households to report information accurately, the probability of being caught would have to be high and the penalty severe, while the public awareness of both of these facts would have to be widespread, enough so that households would take them seriously. This voluntary compliance prompted by the individual’s fear of getting caught.
UNHCR can rely on ProGres data (the Profile and Registration Global System) to target its assistance. Thus, there are needs for revising ProGres to allow for the regular collection of vulnerability indicators. There are obvious advantages to maximizing the collection of data during the registration or verification interview (economizing on staff time and other costs, reducing the burden on the refugees themselves, timeliness of data collection). There also some limitations for relying on registration interviews they are usually taken place soon after refugees arrive in the host country and often before they know where and how they will live, or what work and other income sources they may be able to access. This is not the best time to assess their economic vulnerability. If socio-economic questions are to be added to ProGres, UNHCR should consider applying them in verification interviews (after people have been in country for 6 months), rather than at initial registration.
If UNHCR depends on UNHCR registration, refugees should re-register after 6 months of their initial registration where data should be updated (such as place of residence and occupation status) with additional vulnerability indicators (such as housing conditions, exposure to risks and transfers received). Refugees are required to approach the office for updating data every 18 months, where their PMT scores are revised and thus their eligibility status. Refugees have to report of any changes of their data at any time. (See desirable features of the data collection process section.
It is also recommended that during the first 6 month of refugees’ initial registration, Asylum seekers should receive universal initial) urgent) transfers payments. Refugees should be aware that payments are provided as emergency aid but after 6 months their vulnerability status are reviewed and other eligibility criteria are to be applied. It should be noted that duration of asylum is taken into consideration while assessing refugees’ vulnerability scores and hence their eligibility.
22
Source: Castaneda. 2005
Table 4: Relative Advantages of Different Data Collection Processes: Outreach Survey vs. On-Demand Application Approaches
Outreach Survey Approach On-Demand Application Approach
Relative Advantages
• Better chance to reach poorest who are less informed
• Lower marginal registry costs (per household interviewed) due to economies of scale for travel costs
• Lower total costs due to self-selection of non-poor out of registry process (interviewing fewer non-poor households)
• Dynamic, on-going entry, easier to update
• More democratic nationally: anyone has right to be interviewed at any time
• Permanent process helps build and maintain administrative and logistical structures
Best Suited
• In areas with high poverty rates (over 70%) and/or high poverty density
• In homogeneous areas (rural areas, urban slums)
• With new registries (programs), particularly when need to start large program quickly
• In areas with low or moderate poverty• In heterogeneous areas• When registry is well known or well
publicized (and outreach campaigns encourage applications in poor areas)
• When people have higher education levels
Examples of Use:
• Colombia (surveying of pre-identified poor areas)
• Brazil• Chile until early 1990s• Mexico PROGRESA in rural areas
• US• Chile’s since early 1990s • Partial Use in Mexico (urban areas)• Colombia (also available on-demand)
23
Desirable features of the data collection process include4:
• Transparency: The process should be designed so that it promotes credibility of the system, via fair and equal treatment of anyone who applies for entry. Both in principle and in practice, modern systems allow any family to register in the unified household registry at any time - with the explicit understanding that registration does not guarantee benefits. Procedures for application and entry should be clearly defined and publicized.
• Dynamism: Registration should be continuous and open, allowing households to apply at any time. This dynamism is particularly important if the programs that use the unified registry for eligibility decisions are intended to serve the newly (or transient) poor (in a safety net function - to “catch them when they fall”). Household circumstances change, particularly when faced with shocks. Generally, on-demand registration processes tend to be more dynamic, with the permanent network of local welfare offices favoring an open registry process. Refugees arrive with savings that will be depleted. They may gain access to employment that can change their economic situation overnight, particularly when he/she arrives with an education and skills, or conversely lose access to employment due to host government regulations.
• Outreach to the (potentially) poor: Specific efforts should also be made to reach out to register potentially poor households. Public awareness campaigns (using media commonly accessed by the poor, such as radios) should publicize registration procedures and entry points.
• Cost efficiency: At the same time, efforts should be made to minimize the cost of interviewing, while ensuring the integrity of intake efforts. Collecting the information in an office reduces the time and travel costs of the social workers. It can also speed up the application process considerably by allowing the social worker to enroll the individual directly at the time of the interview. However, the costs savings of collecting information in this way should be weighed against the likely degree of misreporting and the costs of leakage. An intermediate solution might be to collect the information in the office, and to make household visits and verifications for a sample of beneficiaries. Households that had given inaccurate information would then have to pay some sort of penalty. Penalties can range from turning off payment smart card, repaying benefits households received or denying them from receiving other benefits such as WFP vouchers. MoSS tried these options but they were successful only with turning off payment smart cards.
• Administrative feasibility: The data collection process also needs to suit the administrative capacities of UNHCR charged with implementing it. There are great needs in field staff skilled in interviewing and data-collection.
• Communications: Communications strategies also play an important role in ensuring a transparent, credible and quality data collection process. Communications strategies should be designed to promote awareness of the registry itself (outreach tool), as well as of key aspects of the registration process.
4 This part of the study depends on literature review about countries’ experiences in implementation cash transfer programs. Among literatures that were used are; Castaneda and Lindert 2005, Grosh et. al. 1998, and Coady et. al., 2004.
24
2.2. Managing Unified Household Information Systems
Once household data are collected, they are entered into the database of the unified household information registry. Several practical, operational and technological factors influence the management of unified household information systems.
• First, a consolidated national database is important and can help avoid duplications and track beneficiaries.
• Second, proper identification of individuals is crucial. Moreover, software and coding systems need to be designed to link individuals with particular families (or assistance units).
• Third, updates and re-certifications are important for tracking fraud and avoiding situations which can emerge as registries become dated. They also allow for turnover in beneficiaries, to make space for other poor families to enter the registry (and programs). Three types of updates are generally needed with household assessment mechanisms: (a) updates of any changes in household composition and location (address); (b) updates of changes in economic status or income; and (c) full-fledged re-assessments of all variables that determine eligibility (re-certification).
• Fourth, database management should be designed to be able to flexibly respond to changing policies and updates and rely on common software (even if data entry is decentralized) with pre-testing of systems, well-designed manuals, and adequate training for users.
2.3. Monitoring, Verification, and Fraud Control
The use of sound instruments for monitoring household registries, verifying information, and controlling fraud are very important for the performance of unified household registries. It is important to note that no system is 100% immune to manipulation and fraud. Even in countries with sophisticated verification and fraud controls, leakage to the non-poor and fraudulent benefits occur and are routinely uncovered.
A number of mechanisms for monitoring systems, verifying information and controlling fraud may be developed including; supervision of interviews (in the field or in offices); random-sample re-interviews; citizen oversight.
25
2.4. Determining Household Eligibility by Calculating Composite PMT Scores
Once data are collected, they are entered into a unified household registry and cleaned. Household PMT composite indices (scores) are then constructed using the data collected on the variables in the index and the pre-determined weights. This construction is usually an automated process, having been programmed into the registry software. These household PMT scores are then compared to previously-established eligibility cut-offs (thresholds) for particular social programs. These cut-offs can be specific score levels or ranges of scores. Once eligibility is determined by comparing household scores to the eligibility thresholds, program-specific beneficiary lists (sub registries) are created for the purposes of program implementation and payroll.
Borderline cases will always be difficult to judge, some people will always fall just outside wherever the line is drawn. If this type of targeting is found to be excluding large numbers of people who are judged by other criteria or cross-checks to be in real need of the assistance offered, there is probably a strong argument for adding to or adjusting the criteria or ensuring there are adequate safeguards or supplementary targeting methods to catch them.
• Establish Appeals Framework
To mitigate the exposure to risk caused by exclusion and to acknowledge potential inaccurate predictions, Appeals mechanisms have to be established to allow refugees to request a re-evaluation of their eligibility for assistance after exclusion, or to reconsider the amount of payments.
Community may be able to play a helpful role in this. Communications strategies also play an important role in ensuring awareness of the registry itself and eligibility (targeting) process, as well as of key aspects of the registration process.
MoSS established a GRM (Grievance Redress Mechanism) to allow any applicant to file a grievance about either the enrollment process (usually when the applicant thought that he/she should have been eligible but was rejected) or the payment process (usually when the beneficiary thought that he/she should have received a different payment amount or at a different time) under the Takaful and Karama Program.
Complaints or grievances may also be filed when someone believes that a recipient of the Takaful and Karama Program should not be receiving payments. A GRM is established in all Social Units that interface with the public. The GRM are prominently displayed and widely publicized. Complaints could be made at the Social Unit in person or in writing and will be filed and managed using the ‘complaints’ module of the MIS.
26
• Leverage local statistical expertise
UNHCR should establish and continue engaging with a cadre of experts to provide consistent technical support on survey design (questionnaire and sampling), developing analytical models, and statistical and econometric analysis of survey data and in the analysis and development of vulnerability scores for targeting. Thus, it is recommended for UNHCR to continue to pursue and capitalize on partnerships with local experts in academia, private sector, national statistical agencies4.
2.5. Monitoring and EvaluationDuring implementation, there are needs for great care in its operation, great emphasis to avoid that its aims get weakened or corrupted, and a lot of persistence to achieve its short and medium-term objectives. It is essential to carry out a continuous and permanent monitoring, and a constant effort at correcting possible deviations and abuses, as well as to improve its operation and adapt it to changing or unexpected situations.
It is well established fact that cash transfer programs should be accompanied by systematic efforts to measure their effectiveness and impact and to document and publish the monitoring and evaluation results, in order to be able to make a convincing case for their continuation and expansion. UNHCR evaluation design should be conceived from the beginning as part of the design of the program. Not only does the evaluation benefit from being built into the project, but also the discussion of “what to evaluate” can sharpen the focus of the program.
4 On a global level, UNHCR is increasing its capacity and also pursuing strategic partnerships with international institutions. For instance, UNHCR and World Bank have an MoU for a joint-data center. http://bit.ly/39ADaav
UNHCR/Pedro Costa Gomes
27
2.6. Setting Graduation Framework
Throughout cash transfers program, it is necessary to adjust transfer parameters in order to reduce potential dependency55. Several decisions have to be undertaken by policy decision makers:
2.6.1. Maximum Time Limits
Maximum time limits for cash transfer benefits have been actively used, for example it is two years in the Chile, US (five years), and Mexico (7-9 years) though with widely different time horizons reflecting different perceptions about the nature of poverty being addressed (transient vs. chronic).
2.6.2. Defined Exit Thresholds
Another decision that should be discussed in the context of UNHCR is the possibility of defining an “exit threshold” for scores that is somewhat higher than the “entry threshold”. Further research is needed to explore both the desirability and feasibility of these exit thresholds, particularly vis-à-vis other options (such as gradual benefits reductions).
2.6.3. Gradual Benefits Reductions
Gradual benefits reductions (over time and/or with increases in incomes) could apply to the UNHCR cash transfer. Chile and Mexico have both introduced a gradual reduction of cash benefits over time, leading up to an ultimate phase out associated with a specific time limit. The US has introduced a different type of gradual benefits reduction: one in which benefits only gradually fall with increases in incomes (to reduce the degree of “marginal taxation” on earnings).
5 This challenge was also noted in the UNHCR-World Bank report where graduation and dependency are key challenges for UNHCR as most jurisdictions do not allow refugees to work or access micro-finance, as such sustained graduation out of poverty may not be feasible.
28
Outreach Exit
Eligibility determination
Re-certification
Beneficiaries Payments
Appeal
Figure 3: Key Phases Influences Targeting Accuracy for Transfer Programs
29
Annex A
1
UNHCR Egypt Targeting Modality for Cash for Basic Needs
General Information (Household)
Date of Interview: / / Questionnaire No.:----------------------- Name of household head: Phone number:
Nationality Language
Passport No./ Number of HH members:
Address Information
Governorate
District
Town/Village
GPS coordinates
Detailed address Street Name Building Floor Apartment No.
Table 1: LIVING CONDITIONS
1) Type of housing
1. Apartment / villa / house 2. Separate room in a shared apartment 3. Unfinished shelter / basement / garage Workplace 4. Collective shelter
2) Type of occupancy
1. Owned apartment/house 5. Assistance 2. Furnished rental 6. Hosted (for free) 3. Unfurnished rental 7. Squatting 4. Provided by Employer
3) Living space in m2 (Occupied by your HH) _____ 4) Number of rooms (bedrooms and living rooms) __________
5) How many people share the same house/ flat? ___ __________
6) Primary wall material 1-brick / cement/ concrete/ stone 2- Other
7) Primary flooring material 1-Tiles/ marble / ceramic-tiles 3-cement/concrete 2-vinyl /wood (parquet) 4-dirt /other
8) Primary roofing material 1-reinforced steel/ metal/cement 3- other 2-wood 4- (no roof)
9) Source of Drinking water 1- Public network 2-Water pump 3-Other
10) Dwelling access to water supply 1-tap inside dwelling 2-tap outside dwelling 3-other
11) Availability of toilet 1-Private 2-shared 3-None
12) Type of toilet 1-Modern with flush 2-tradition with flush 3-tradition without flush 4-None
13) Sewerage system 1-Public network 2- Private network 3- trench 4- In the street (no sewage system) 5-other
14) Availability of kitchen 1-Private 2-shared 3-None
15) Primary fuel used in cooking 1-Butagas 2- natural gas 3- Electricity 4- Kerosene 5-Other
16) Primary garbage disposal method 1-Garbage collector 2- Cleaning company 3-Other
Table 2: DURABLE GOODS
Item No 0 Yes 1
Item No 0 Yes 1
Item No 0 Yes 1
Private Car Smart Phone Computer/Laptop/Tablet
Motor Bike Internet Full Automatic washing machine Television Water Heater Semi-Automatic washing machine Air Condition Refrigerator
30
2 T
ab
le 3
: HO
US
EH
OLD
BIO
DA
TA
Ta
ble
3: c
on
tin
ue
d
T
ab
le 4
: SO
UR
CE
OF
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AN
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ER
S
N
o.
Na
me
Dis
ab
ility
0
. No
1.Y
es
Sp
ecif
ic N
eed
s
1.S
ing
le p
are
nt
2.C
hild
at
risk
3
.Old
er p
erso
n a
t ri
sk
4
.Un
der
ag
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arr
iag
e
5
.Wo
ma
n a
t ri
sk
Pa
ssp
ort
No
./ID
N
o.
Reg
iste
red
w
ith
UN
HC
R
0.N
o
1.Y
es
P
lea
se s
pe
cify
th
e s
ou
rce
of
tra
nsf
ers
yo
u r
ece
ive
d
du
rin
g t
he
la
st 3
0 d
ay
s fr
om
e
ach
of
the
fo
llo
win
g
sou
rce
s
0.N
o
1.Y
es
Hea
d
WF
P F
oo
d a
ssis
tan
ce
2
3
(v
ou
che
r)
4
Ca
rita
s (c
ash
)
5
Pe
nsi
on
6
Mo
ne
y s
en
t by
fam
ily o
r fr
ien
ds
fro
m a
bro
ad
7
8
Mo
ne
y s
en
t by
fam
ily a
nd
fr
ien
ds
in c
ou
ntr
y
9
10
No
. N
am
e S
ex
0
. Ma
le
1.
Fem
ale
Ag
e
In
com
ple
ted
yea
rs
Ma
rita
l Sta
tus
1.S
ing
le/E
ng
ag
ed
2
.Ma
rrie
d
3
.Div
orc
ed/s
epa
rate
d
4.W
ido
wed
5
.Oth
er
Sch
oo
l E
nro
lme
nt
(c
hild
ren
3-
17 y
ears
)
0.N
o
1.
Yes
Ed
uca
tio
n S
tatu
s (t
en
yea
rs a
nd
ab
ove
)
1.b
elo
w a
ge
2
.Illit
era
te
3.B
asi
c
4.S
eco
nd
ary
5.T
ech
nic
al h
igh
er/
Hig
her
Em
plo
ym
ent
Sta
tus
(6 y
ears
a
nd
ab
ove
)
1.O
ut
of
Lab
or
Fo
rce
2
.Sel
f-em
plo
yed
3.W
ag
e em
plo
yed
4
.Un
emp
loy
ed (
loo
kin
g f
or
wo
rk)
Sta
bili
ty in
w
ork
1.P
erm
an
en
t
2
.Tem
po
rary
Co
ntr
ibu
te
to
ho
use
ho
ld
inco
me
0.N
o
1.
Yes
Hea d
2
3
4
5
6
7
8
9
10
31
Dr. Heba Farida Ahmed Fathy El-laithy is currently a Professor of Statistics at Cairo University’s Faculty of Economics and Political Science. She is specialized in micro data analysis and Socioeconomic and Social impact evaluation for development programs. Dr. El-laithy has a strong background in statistical analysis. She received her PHD Degree from Sussex University in United Kingdom. She is a key poverty and inequality expert who has contributed her expertise to several poverty assessments and poverty alleviation program evaluations in Egypt, Lebanon, Syria, Yemen, and Rwanda, authoring and co-authoring seminal reports on poverty, inequality poverty alleviation and social safety net development in MENA region for governments, research centers and national and international organizations such as UNDP, WORLD BANK, ESCWA and ECA. She also developed two toolkits for measuring inequality for Arab countries and Africa. Dr. El-laithy has published papers on issues including the gender dimension of poverty, social risk management, and monetary poverty, multidimensional poverty and food security. She led the team for developing poverty map for Egypt, using household Income, Expenditure and Consumption Survey 2017/18 and Population and housing Census 2017. Since 2015, she participated in designing targeting mechanisms for new cash transfer programs of Ministry of Social Solidarity, based on Geographical Targeting and Proxy Means Testing.
About the Authors
Dr. Dina M. Armanious is currently a Professor of Statistics at Faculty of Economics and Political Science, Cairo University, Egypt. She has a strong background with more than 20 years of experience in household survey data analysis, Food security assessments, Income and Multidimensional Poverty Analysis in Arab countries, Impact Evaluation and Poverty Maps estimation. She is co-authoring reports on poverty, inequality, food security, poverty alleviation, child poverty and social safety net development for UNICEF, WFP, ESCWA, UNDP and League of Arab States. She contributed as co-author in poverty assessments and poverty alleviation program evaluations in Egypt, Lebanon, Syria, Yemen and Oman. She has published papers on issues including monetary poverty, multidimensional and child poverty, gender dimension of poverty, Social protection polices, food security and targeting and health care utilization. Recently she authored the following: “Impact of the changes in women’s characteristics over time on Antenatal Health Care Utilization in Egypt (2000-2008)” (2015), Journal of Obstetrics and Gynecology, 2015, vol. 5, 542-552 (under Thomson Reuters Journals), and “Mobility in Income Poverty Between 2010 and 2015 in Egypt”, (2019) World Economics Vol. 20 No. 1 January -March 2019.
32
AcknowledgmentThe authors would like to appreciate UNHCR Representative in Egypt, Mr. Karim Atassi and his management team for the support provided to finalize this report. We also appreciate support from UNHCRs Egypt Head of Operations, Mr. Emile Belem and the Cash Based Interventions and Data Analysis and Management teams particularly Mr. Steven Choka, Ms. Aliaa Rashwan and Mr. Timothy Mutuerandu who contributed to this report. Overall, appreciation is also extended to all refugee all households who participated in the survey and Caritas Egypt which conducted the data collection.
1. Castañeda, T. (2005) “Targeting Social Spending To The Poor With Proxy–Means Testing: Colombia’s SISBEN System,” Social Protection Discussion Paper Series No. 0529, The World Bank.
2. Coady, David, Margaret Grosh and John Hoddinott (2004). Targeting of Transfers in Developing Countries: Review of Lessons and Experience. Washington, D.C.: The World Bank. http://www.worldbank.org/lacsocialprotection.
3. Grosh, M., and E. Glinskaya. 1998. “Proxy Means Testing and Social Assistance in Armenia. World Bank Development Unit, Country Department III., Europe and CentralAsia. Washington, D.C.
4. Lindert, Kathy (2005). “Brazil: Bolsa Familia Program – Scaling-up Cash Transfers for the Poor” in Managing for Development Results Principles in Action: Sourcebook on Emerging Good Practices.
5. United Nations High Commissioner for Refugees (UNHCR). 2014. “Syrian Refugees Living Outside Camps in Jordan. Home Visit Data Findings 2013.” UNHCR, Geneva.
6. Verme. P., C. Gigliarano, C. Wieser, K. Hedlund, M. Petzoldt and M. Santacroce (2016). “The Welfare of Syrian Refugees, Evidence from Jordan and Lebanon” World Bank and UNHCR.
References
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