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Poverty and Transfers in Yemen

by

Dominique van de Walle∗ Development Research Group

The World Bank

December 2002

Discussion papers are not formal publications of the World Bank. They represent preliminary and often unpolished results

of country analysis and research. Circulation is intended to encourage discussion and comments; citation and the use of the paper should take account of its provisional character. The findings and conclusions of the paper are entirely those of the authors and should not be attributed to the World Bank, its affiliated organizations, or to members of its Board of Executive Directors or the countries they represent.

∗ The author wishes to thank Lamis Al Iryani, Amat Al Sharki, Dorothyjean Cratty, Martin Ravallion, Setareh Razmara and Giovanni Vecchi, as well as all government officials met during a January 2002 mission to Sanaa.

Table of Contents

Summary I Introduction ............................................................................................................................... 1 II Government Programs............................................................................................................... 3

i) The Social Welfare Fund (SWF) ............................................................................. 4 ii) Diesel Subsidies...................................................................................................... 8 iii) The Agriculture and Fisheries Production Promotion Fund (AFPPF)................... 8

III Donor–Assisted Programs ......................................................................................................... 9 i) The Social Fund for Development (SFD)............................................................... 10 ii) Public Works Project (PWP) ...........................................................................12 iii) Poverty Alleviation and Employment Program (PAEG)...................................... 13 iv) The World Food Program (WFP) ......................................................................... 14 v) The Southern Governorates Program (SGP).......................................................... 15 IV Assessing the Regional Targeting Performance of Poverty Programs .................................... 15 V Overall Assessment and Recommendations:............................................................................ 17 References...................................................................................................................................... 20 Tables Table 1. Poverty and safety net programs in Yemen................................................................... 22 Table 2. Distribution of net public and private transfers in 1998 under different assumptions about the propensity to consume out of transfers (annual YR per capita)..................... 23 Table 3. Percent of population living in households who received public and private transfers in 1998 ............................................................................................................ 24 Table 4. Public and private transfers as a share of household expenditures................................ 25 Table 5. Incidence of the percentage of individual transfers in total public and private transfer income in 1998 (YR per year per capita) ...................................................................... 26 Table 6. Incidence of transfer incomes (% of population)........................................................... 27 Table 7. Estimated SWF target population and coverage in 1999 (%)........................................ 28 Table 8. Incidence of SWF payments in 1999 (YR per year per capita and % of population) ... 28 Table 9. Percent of population with amenities and living in an area with various services in 1999 ............................................................................................................. 29 Table 10. Population possibly consuming diesel through use of generators for lighting or irrigation in 1999 ........................................................................................................... 30 Table 11. Program performance in targeting the poor across governorates................................... 31 Table 12. The SFD’s performance in targeting the poor across governorates under phases I and II ($US/per household) ............................................................................. 32

خالصة

وتستخدم . تتم مراجعة وتقييم برامج اليمن الخاصة بشبكات األمان ومكافحة الفقر بقدر ما تسمح البيانات المتاحة وتشير األدلة إلى . بيانات األسر المعيشية الحديثة لدراسة مدى انتشار البرامج وتوجيهها نحو المناطق الفقيرة والفقراء

ويبدو أن معظم البرامج تهتم بالفقر في المستقبل أكثر من الفقر . وجيهها بشكل عام انخفاض نطاق تغطية البرامج وسوء ت وهي تركز على توفير فرص طويلة األمد للفقراء من خالل تنفيذ مشروعات بنية أساسية صغيرة النطاق، وتحسين . اليوم

. اء والعرضين للمعاناة في الوقت الحاليوهناك تركيز أقل على الفقر. تعليم البنات والخدمات المقدمة للمجتمعات المحليةغير أنه مثقل حاليا بالقيود . ويمكن أن يستثنى من ذلك صندوق الرعاية االجتماعية، بالنظر إلى األهداف التي يتوخاها

م ونقد. اإلدارية، واستجابته بطيئة، ونطاق تغطيته محدود، لدرجة أنه ال يحدث أثرا يذكر على من يعانون من الفقر حاليا . توصيات بشأن تحسين شبكات األمان االجتماعي اليمنية

Résumé

Le filet de protection sociale et les programmes de lutte contre la pauvreté au Yémen sont examinés et évalués dans la mesure que le permettent les données disponibles. Les données récentes sur les ménages sont exploitées pour étudier l’incidence des programmes et leur ciblage sur les zones défavorisées et les pauvres. Les faits observés montrent une faible couverture du programme et un ciblage généralement insuffisant. La plupart des programmes semblent accorder plus d’importance à la pauvreté dans le futur qu’à la pauvreté actuelle. Ils portent sur l’offre d’opportunités à long terme pour les pauvres au moyen de la construction de petits projets d’infrastructure, de l’amélioration de l’éducation des filles et des services communautaires. Les efforts du programme sont moindres pour aider ceux qui sont pauvres et vulnérables maintenant. Le Fonds d’aide sociale constitue une exception potentielle en raison de ses objectifs. Malheureusement, il est à présent trop pesant sur le plan administratif, trop lent à répondre et sa couverture est trop limitée pour avoir un impact sur les pauvres d’aujourd’hui. Des recommandations sont faites pour améliorer le filet de protection sociale du Yémen

Abstract

Yemen’s safety net and poverty programs are reviewed and assessed to the extent permitted by available data. Recent household data are exploited to examine the incidence of programs and their targeting to poor areas and poor people. The evidence points to low program coverage and generally poor targeting. Most programs appear to weigh poverty in the future more highly than poverty today. They focus on providing long-term opportunities for the poor through building small-scale infrastructure projects, improving girls’ education and community services. There is less emphasis on helping the currently poor and vulnerable. One potential exception, given its objectives, is the Social Welfare Fund. Yet it is at present too administratively cumbersome, slow to respond and of limited coverage to make much of an impact on today’s poor. Recommendations for improving Yemen’s social safety net are made.

I Introduction

This paper examines the safety net and poverty programs that are found in Yemen. Following a brief description of private transfers and their incidence, the paper concentrates on public transfers, though excluding formal pension schemes (which will not be considered part of the safety net here). It provides a description of the main public programs and critically assesses their coverage and targeting to the extent possible. In particular, the paper exploits recent household level data to see what they reveal about the incidence and targeting of transfers to poor areas and poor people. Unfortunately, this exercise is limited as the surveys collected very little information on participation in public programs. Furthermore, little can be concluded concerning program impacts on poverty since no impact evaluations have been conducted.

There are two household surveys: the 1998 Household Budget Survey (HBS) and the 1999 National Poverty Survey (NPS), with a number of differences between them. The HBS is a traditional consumption survey which collects exhaustive expenditure and income data over a full year but scant information on other aspects of well-being. In contrast, the NPS contains a wealth of information on non-income facets of living standards, but is not ideal for measuring expenditures or incomes. The NPS was completed during the course of one month only, so that it does not capture welfare variability due to seasonality. Also the consumption data in the NPS is based on last week’s expenditures compared to the HBS’s focus on the last month with specific questions on each of the last 4 weeks. Finally, the NPS covers far fewer consumption items than the HBS. For example, the HBS collects consumption on 20 cereal products compared to 9 in the NPS. We would thus expect total expenditures to be less well measured in the NPS. This may influence the ranking of individuals in the distribution of welfare with implications for conclusions about incidence and targeting of transfer incomes. Against that, comparisons of transfer amounts identifiable in both surveys indicate remarkably similar totals as well as distribution. One would also expect regular payments such as from the government’s Social Welfare Fund to be more accurately collected in the NPS than irregular income sources.

Use is made of both surveys here, with preference given to the HBS when information is available from both sources. However, caveats about the NPS should be kept in mind. Below the population is ranked into national deciles by household per capita expenditures. Hence deciles are comparable whether reference is being made to the rural, urban or national populations. However, deciles are not strictly comparable across the two surveys. Unless otherwise noted monetary amounts are expressed in annual per capita riyals (YR).

The key to determining whether transfers reach the poor is to assess what their welfare would have been without those transfers. Only then can we know the distributional impact by seeing the incidence of transfers according to how poor people would have been without them. First, an appropriate indicator is needed to identify the poor. Outcomes may depend on that choice. Studies of the incidence of public spending often subtract the entire amount of government transfer receipts from household income or consumption to approximate pre-intervention welfare, and so rank the population into deciles (say). In the following analysis we will follow this common practice. However, this assumes that there is no replacement through household behavioral responses. That assumption is implausible. The opposite assumption — treating post-transfer consumption as the welfare indicator for assessing incidence — is just as questionable. Ideally, one would like to subtract the intervention amount but add in the replacement income households would have achieved through their behavioral responses had they not benefited from the intervention. In the few studies for other countries (Hungary and Viet Nam), the estimated marginal propensities to consume out of transfer income have been around 0.5 (van de Walle et al. 1994 for Hungary, van de Walle 2002 for Viet Nam). Jalan and Ravallion (2002) also estimate about 50% income replacement for public transfers in Argentina. We will test transfer incidence sensitivity by

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using this estimate as well as the two others to determine the net gain to consumption from public and private transfers and to construct alternative counterfactual consumption levels.

Table 1 outlines the sources of private and public transfers available to Yemeni households. Remittances from relatives abroad are by far the largest source of private transfers. Although a large drop in remittances occurred after the massive repatriation of Yemeni workers from the Gulf in 1991, these have slowly picked up once again. Domestic inter-household transfers from relatives and friends are also significant. This includes outlays from individuals (primarily male relatives) to their dependents (usually ‘unsustained’ female relatives and their children) whom they are legally responsible for supporting. It is unclear how well this system works and what the compliance rate is. Zakat (and Sataqa) are religiously stipulated charitable contributions. Public transfers come from both government programs and from donor-initiated and financed projects. These are detailed below. The only government transfers that can be specifically identified in the HBS are from pension and retirement accounts, while the NPS allows an identification of Social Welfare Fund receipts.

Table 2 provides an overview of the net per capita monthly money amounts of transfers identifiable in the 1998 HBS data a small subset of those listed in Table 1. The amounts represent mean total income from Zakat, domestic and foreign cash and in-kind remittances, transfers from other government organizations and pension and retirement income. Netted from this total are the amounts paid out by the household for Zakat duty, donations and gifts to friends and family and transfers to dependents.1

Table 2 shows the sensitivity of the incidence of mean per capita net transfers across deciles

under different assumptions about the propensity to consume out of transfer income, namely fully excluding, including half only and fully including transfer incomes when assigning households to pre-intervention deciles.

Conclusions about targeting and incidence clearly depend on how the counterfactual is defined. Concentrating on deciles defined on per capita expenditures net of transfers in the first 3 columns of Table 2, the results suggest that net transfers are rather well targeted to the very poorest households. The bottom decile benefits from the largest mean per capita amounts and interestingly, gets five times more than the next poorest decile. All other deciles receive much less. Mean transfers equal 10% of the lower poverty line nationally, ranging from 9% in rural areas to13% in urban areas. By contrast, transfers to the national, rural and urban population in the poorest decile respectively equal 45, 38 and 86% of the value of poverty lines. In general, there is evidence of urban bias: each decile’s urban population receives a larger absolute amount.

Focusing instead on deciles defined on the basis of post-transfer expenditures (the last 3 columns), the incidence pattern across deciles is strikingly different with transfers rising with welfare, and a significant concentration of transfer income in the richest decile. When deciles are formed netting out half of transfers, there is still a concentration of transfer incomes in the poorest decile though the amounts are lower, and more is going to the higher deciles. A possible explanation for the reversal of transfer concentration from bottom to top decile depending on the decile definition is measurement error. For example, if some transfers have been erroneously inflated they could dwarf other expenditures so that when they are subtracted (included) from total expenditures, their recipients fall in the lowest (highest) 1 Unfortunately, the HBS lumps income from local remittances and transfers from ‘other government organizations’ together. We do not know what is contained in ‘other government organizations’ but this could well include payments from the government’s social welfare fund, for instance. We cannot tell.

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decile. To test for this possibility, I trim off the top 2 percent of transfers under the assumption that these reflect measurement errors. This results in an attenuation of the amounts going to the top and bottom deciles but little change in pattern across deciles. This seems to indicate that the general pattern is correct.

Table 3 presents information on the percentage of the population living in households that

received these income transfers in 1998. Nationally, 33 percent of the population received transfers though that ranged from less than a third of the rural population to almost half of the urban population. Coverage is highest in the poorest decile at 57 and 80 percent of the rural and urban populations respectively when deciles are defined based on pre-transfer expenditures. There is a monotonic decline as welfare rises, but coverage is still high at 19 and 38 percent of the rural and urban populations in the richest decile. When the post-transfer deciles are used, the distribution of beneficiaries is much flatter with only small differences across deciles.

Table 4 presents total public and private transfers as a percentage of household expenditures.

Together, transfers make up around 8 percent of total expenditures for the average Yemeni household. However, transfer incidence is highly progressive, exhibiting an almost monotonic decline as welfare rises. Transfers account for 38 percent of total expenditures for those in the poorest pre-transfer decile nationally. This rises to 56 percent for those in urban areas and drops slightly to 35% for those in rural areas. They are clearly a quite substantial source of livelihood for the poorest Yemenis. As can be observed in Table 5 which shows the share of individual transfer sources in the total the bulk of these transfers come from private sources, primarily foreign and local remittances. The public transfers that can be identified in the surveys are small and account for a maximum of 41 (if there are no local remittances) and a minimum of 10 percent of total transfers (no transfers from ‘other government’ organizations). However, retirement and pension transfers account for a much larger percent of total transfers in urban areas at a mean of 16%. Table 6 also shows that while 8% of the urban population lives in households that receive income from this source, only 2% of the rural population does so. The other significant difference between urban and rural areas is in the incidence of Zakat transfers that clearly favors the urban population with 27% receiving them versus 8% in rural areas.2

Finally a word about transfers to and from dependents. The HBS does not specifically identify

such transfers on the income side as they are lumped together with other local remittances and donations from ‘other government organizations.’ However, it does identify them on the expenditure side. Subtracting the mean amounts paid out from mean income from the HBS category ‘local remittances and transfers from other government organizations’ reduces the total amount by 7% nationally, 3% and 15% in rural and urban areas respectively. Thus, these transfers are either quite small or underestimated in the HBS’s expenditure data.

In conclusion, the above results suggest that the urban population is favored in terms both of coverage and absolute transfer amounts, and that foreign remittances are the most significant transfers among those that we can identify in the data.

II Government Programs

Intent on minimizing the social impacts of an adjustment and reform program, the government instituted a number of social programs in 1995/96. Thus, the programs are relatively recent. Below we discuss the government’s key social assistance programs: the Social Welfare Fund and the Agricultural

2 UNDP 1998 notes that Zakat is increasingly treated as general taxation and used to finance general infrastructure and services. It is not clear how this works or what it means for interpretation of the incidence data.

4

and Fisheries Production Promotion Fund. There continues to be a subsidy on diesel in Yemen which is claimed to be pro-poor. This is also discussed briefly. A number of other small schemes including the Martyr’s Welfare Fund (also known as the War Veteran’s Fund) which provides assistance to veterans of the 1962 war, and the Tribal Authorities Fund which transfers resources to tribal leaders, are difficult to get information about and are therefore not discussed. i) The Social Welfare Fund (SWF):

The SWF is the government’s main targeted social assistance program. It was originally conceived in 1996 as a way to compensate the poor for the removal of subsidies. Having little prior experience with this kind of program, the country has struggled with how to best select and reach beneficiaries. An assessment by the World Bank in 1997 found that the SWF suffered from a lack of clear norms, followed extremely bureaucratic procedures with very little follow-up on targeting and beneficiaries, and that too little spending on the program’s administration rendered the program particularly weak in rural areas. A field visit revealed poor targeting, widespread ignorance about the program and problems in the distribution of transfers with evidence of middle men pocketing part of the (already small) payments. The SWF’s overall budget for beneficiary transfers and administration has also been low.

Under the SWF, cash transfers of YR1000 per beneficiary, plus 200 for each additional dependent up to a maximum of YR2000 per household per month are available to 15 different target groups. The payments are made on a three monthly basis, so that a household receives a maximum of YR24,000 a year. For a family of 6 this translates to about YR333 per person per month or only about 10% of the 1998 national poverty line. Over YR 8 billion were spent on the SWF in 2001 to the benefit of 450,000 households.

There is a first stage of geographic targeting. The SWF’s Board of Directors decide how many cases they can afford each year and allocate case shares to each governorate based on the incidence of poverty, the share of the country’s population and cases of pre-SWF assistance.3 The governorates are in turn responsible for distributing the cases to the districts on the basis of lists of the eligible. These allocations are likely to be influenced by political considerations.

Given the geographical coverage, the program targets the following groups: 1) orphans; 2) widows with children; 3) widows without children; 4) divorced women with children; 5) divorced women without children; 6) single women; 7) the fully disabled; 8) the partially disabled; 9) the poor; 10) the elderly; 11) the temporarily fully disabled; 12) the temporarily partially disabled; 13) families with a missing head of household; 14) families with an imprisoned head of household; and 15) families with a head of household recently discharged from prison. In addition to falling into one of these groups, recipients must also be deemed to be without income (income must be the benefits from the SWF) or income earning potential. This means that those already receiving assistance from the Martyr’s Fund, or receiving a pension, for example, are in principle not eligible. There is an attempt to respond to shocks by allowing for both temporary and permanent cases. Beneficiaries in groups 1 through 10 above are identified as permanent, while those in 11 to 15 are eligible only for temporary assistance for which yearly renewals cannot exceed three years. The law also provides for lump-sum assistance to households who experience personal emergencies or are affected by covariate disasters.

3 The SWF replaced a smaller social assistance scheme run by the Ministry of Insurance and Social Affairs.

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According to SWF records, in 2000 the largest number of direct beneficiaries were either the poor, or widows with children. This was the case in all governorates.

To receive transfers, potential beneficiaries must fill out applications and provide proof of status

and lack of income or earning potential in the form of documentation and various certificates (birth, marriage, age, disability, police testimonial, etc.). Typically, they must come to branch offices to submit their applications. This procedure clearly penalizes the illiterate, elderly, disabled and remote. Certificates can be hard to come by and applications difficult to complete. In principle, efforts are also made to search out the eligible. Social workers from the local offices verify applications but also help identify potential beneficiaries and fill claims. Local NGOs, local councils and Sheiks may also be active in identifying candidates, drawing up lists and informing those in their communities about the program. Even with these additional efforts, it is not clear that those in isolated rural areas and arguably the most vulnerable, are being adequately reached. Local offices do not have the capacity (manpower, vehicles, information technology, etc) for exhaustive outreach. Yet, reliance on local Sheiks brings about its own worries including the possibility of capture by friends and clients, with outcomes that do not necessarily favor the highest priority cases. Indeed, the Yemen Voices of the Poor provides some evidence that this is occurring (PRSP 2002). In some areas, poor men and women felt that the real poor were not benefiting, that friends of the Sheik were most likely to do so and that in some cases, bribery and corruption were used to get on the rolls. This is clearly region specific as respondents in some other areas felt that the program reached the right households.

At any rate, the SWF budget is generally too low to cover all of the potentially eligible. Even existing applications far outweigh the supply of transfers. (In Table 7 below, we use the HBS to make an estimate of the target group and existing coverage.) A huge backlog of applications and long delays in dealing with applications is common and caused by a lack of personnel and funding together with a burdensome application and substantiation process. All requests must be checked by local office staff before being forwarded for a second check by the governorate and then on to Sana’a for final confirmation or rejection. In 1998, the time between a governorate’s approval and a beneficiary’s first payment took between 6 and 12 months (World Bank 2000d). Many of those who apply are found to be ineligible. Applicants are disqualified for incomplete or misleading applications and rejected if a household member is found begging. In principle, local staff are required to conduct follow-ups every 3 months, as well as yearly when beneficiaries are left or taken off the lists. It is not known how rigorously this is done.

Once an application is approved, there is still the issue of getting payments to beneficiaries. Payments are in the form of checks 4 times a year that can be cashed by local cashiers at the local SWF offices or at post offices. The SWF recently started to rely on agents of the Ministry of Finance as local cashiers in residence in every district. This cashier directly pays the transfers to the beneficiaries. This appears to have improved the payment system. The SWF is also increasingly relying on mail delivery of transfers. This is limited to the larger population centers where there are post offices, but should increase in the future as more post offices open. Despite these changes, many beneficiaries clearly continue to have difficulty in receiving and cashing their checks. Those in remote areas, or who have trouble getting around, are likely to continue to have to rely on middle men who take their cut.

The SWF has instituted a number of other administrative changes in the past few years. It now has branch offices in all governorates, and in 127 districts. Since 2001, the program has acquired and trained 620 ‘researchers’ who are based at SWF sub-branches throughout the country and help people fill applications, verify applications and update lists of the eligible. The number of researchers is growing though they are not always well trained and tend to be overworked. The researchers spend considerable time checking up on the status of those who are already on the lists to see whether they are still eligible.

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One of the WB recommendations was to raise the share of total spending going to program administration to 15%. SWF staff state that operational costs are low at 4% of total costs. Others have estimated the administrative costs to be more on the order of 13%. The latter seems more plausible. For one, the SWF’s figure consists solely of the amount it spends and does not include the costs faced by the post offices and most importantly, the Ministry of Finance. Although they work for the SWF, the cashiers, for example, are both appointed and paid for by MOF. Only their non-salary costs are covered by SWF. Second, given the complex identification and substantiation process one would expect the administrative cost per applicant to be quite high relative to the benefits. In 1997, the direct costs of identifying and monitoring a beneficiary in rural areas were calculated to be as high as YR 1400 for the first year and YR600 in follow-up years (World Bank 1997b). Although these costs may have declined over time as the SWF has become more efficient, the process remains complex and time consuming, so that costs are unlikely to have declined that much.

The SWF management feels that there have been many improvements recently but that follow-up

remains too slow and should be continuous. A system of fool proof picture id cards is planned. Effort is also needed to improve the documentation and linkages between the regions, along with coordination with other social welfare programs.

Assessment of the SWF In Table 7, an attempt is made to estimate the target population from the NPS and match this up with current participation. Our estimates are rough since we can not exactly identify all members of the target groups and can only approximate the way in which the income and asset tests are applied. We define the target group as the population who are very poor (as identified by being in the bottom per capita expenditures decile defined net of SWF payments) and the population who are both poor (as defined as being below the poverty line) and living in households with a severely disabled adult, an elderly man or woman, headed by a widowed, divorced or never married woman. Table 7 shows that 4.2 percent of the target group, or 0.88% of the population, received SWF transfers. 57 percent of those who got the program were not in the target group. Of those who were not in the target group but who got the program, 41percent were non-poor and 16 percent were poor. Table 7 strongly suggests that the complicated targeting mechanisms used by the SWF are not working particularly well.

A survey which collects information on program participation provides an independent test of public program coverage. The NPS indicates far fewer participants than are claimed by the government. According to their records, the SWF spent YR2.4 billion on 290,000 families in 1998 and YR5.2 billion on 350,000 beneficiaries in 1999. The NPS identifies only 51,841 households covering a population of 360,029, as receiving payments in the year from September 1998 to October 1999. Around 5 percent of beneficiary households are found to have more than one direct beneficiary. Payments averaged 5139 YR per person per year in recipient households for a total of YR1.85 billion identified in the NPS. There are a number of discrepancies here between the survey and SWF data which we are unable to explain. While it is possible that survey respondents did not record getting SWF payments, it is unclear why they would not.

Table 8 provides a picture of the distribution of SWF payments and of participation across deciles

defined net of SWF payments. Coverage and amounts received are negligible. The average per capita amount received is worth less than 0.3 percent of the national poverty line at YR 105 annually and only 2 percent of the population lived in households who received payments from the SWF in 1999. Following the pattern of other transfers, there is a concentration of payments and recipients in the poorest population decile, and a subsequent reduction in both as welfare rises. However, there are beneficiary households in every decile. The average amount received in the bottom decile is equal only to 1.2% of the poverty line and only 5% of the poorest ten percent of the population live in households that received SWF benefits. Again, there is an advantage to being in an urban area where per capita payments for the poorest decile

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are nearly three times higher and 9% of the population received transfer payments compared to 4% in rural areas.

Yet, some payments clearly reach the poorest, particularly in urban areas. From a policy perspective, one conclusion could be to simply spend more on the SWF. We can test for the impact of expanding the SWF by increasing the amounts received by current beneficiaries. We increase the total spent by ten times. We find that this would reduce the poverty rate by 0.7 of a percentage point only. This is due both to the low level of payments and the leakage. Of course, such a policy would also need to be financed. Zero cost is assumed above, as might be the case if the expansion were externally financed or entirely financed by the non-poor. But we also experiment with two internally financed scenarios. In the first case, the financing comes from a tax which is proportional to income. This would result in a slightly reduced impact on poverty of 0.6 of a percentage point. If the cost were borne equally by everyone instead, there would be no change in the poverty rate. It is clear that much more would need to be spent on the SWF to lift those who currently receive it out of poverty.

The SWF relies primarily on eligible beneficiaries knowing about the program and applying for it. Have Yemenis heard about the program? According to the NPS only 31% of the population had heard about it in 1999. And this may well overstate the number who understand the eligibility criteria and whether they can apply for the SWF.

The NPS also indicates that only 18% of Yemenis (5% in rural and 56% in urban areas) live in an area with a post office (see Table 9). So, 82% of the population would probably face some difficulty in obtaining benefits, and it is likely that many of these will be among the most needy given that poor people live in areas without a post office. There are also large geographic differences with only 1.9% of those living in Sana’a governorate to 62% of those in Sana’a city having access to a post office. Access to banks is even lower: only 3% of the rural population live in an area serviced by a bank. The poorest groups are likely to have even lower access. This makes one quite pessimistic about using post offices or Bank branches to speed up and facilitate delivery of SWF payments. On the other hand, in some areas, there may be possibilities of working through schools to which 78 percent of the population has access in their areas of residence. Indeed, only 8 percent say they are ‘very far’ away from a basic education school.

The identification and selection of beneficiaries for the SWF follows a protracted and cumbersome process. There are a number of factors militating against the current heavy administrative side of identifying beneficiaries in a poor and rugged country like Yemen. The procedures place a heavy burden on the target groups who may be disqualified simply because they can not get the right certificates together or live too far away or have not heard about the program. And as is apparent in Table 8, the process does not prevent errors of inclusion. Another implication is that the SWF is an ineffective instrument for dealing with shocks since its response rate is much too sluggish. In addition, reliance on local Sheiks may lead to program capture and other problems. There is evidence of this. The program will tend to reinforce the concentration of power and the Sheiks’ local control. It would be better to rely on a countervailing institution instead. Another issue concerns the administrative costs involved in the identification and selection of beneficiaries. This appears to be large relative to the benefits. This does not make sense. Given the stringent eligibility criteria, it is probably impossible for the program to check every case each year, as well as add new cases and not make a lot of errors.

Serious thought should go into how beneficiary selection and final approval could be decentralized to the governorates or even to the district level. This would speed up the application process. The SWF should also consider simplifying its targeting rules. There should be much finer geographical targeting coupled with the status indicators already used. The income and asset tests should

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be dropped. These are easy to manipulate, hard to ascertain and highly variant over time, and at any rate, anyone who truly passed them would be barely surviving. ii) Diesel Subsidies

Diesel is the only consumer good that continues to be significantly subsidized. It appears to be used primarily to run irrigation pumps, electricity generators and fishing boats. Some in the government argue that the subsidy benefits the poor. Indeed, this underlies the rationale for the government’s Agriculture and Fisheries Production Promotion Fund (see below). But others argue that the subsidy has encouraged excessive pumping and inefficient use of water to the detriment of the rural poor.

Are the poor benefiting from these subsidies directly or indirectly? Would they bear the brunt of subsidy cuts? Unfortunately, neither data set directly identifies diesel consumption or the ownership or productive use of irrigation pumps and fishing boats. However by looking at the ownership of power generators, the household use of generators for lighting, and whether irrigation is achieved by means of artesian wells all of which may require diesel it is possible to get a sense indirectly of who the direct consumers and hence beneficiaries of the diesel subsidy are. Of course, there may also be indirect effects through employment and consumption benefits which we cannot ascertain with the available data.

Table 10 presents percentages of the population across deciles who owns an electric generator, for whom a generator is the main lighting source at home, and whose land is irrigated by means of an artesian well as opposed to a lace dam, a spring, floods or ‘other’. The last two columns show the total percent of the population this implies may be directly using diesel. In all cases, the data suggest that the poor probably consume far less diesel than wealthier households. The population percentages consistently rise with the welfare indicator. For example, while 4 percent of the rural population in the poorest decile live in families whose land is mostly irrigated and is irrigated through an artesian well, 20 percent of those in the top decile do so. Two percent of the rural poorest own a generator versus 11 percent in the top decile. Although as many as 6 percent of the rural population in the poorest decile live in households who may directly consume diesel, 28 percent in the richest decile do so.

This evidence is not conclusive but it does suggest that the direct beneficiaries of the diesel subsidy are by and large not the poor. There will no doubt be some poor people who benefit indirectly from the subsidy, though they and other poor people would also benefit (directly and indirectly) from the extra public spending on (inter alia) schools, health clinics or the SWF that eliminating the diesel subsidy could finance. iii) The Agriculture and Fisheries Production Promotion Fund (AFPPF)

The AFPPF was launched in 1995 in light of worries that increases in diesel prices and eventual elimination of the diesel subsidy would affect the poorest population groups in rural and coastal areas who, both as consumers and producers, are highly dependent on agriculture and fisheries. The Fund aims to promote agriculture, livestock and fisheries production through a wide range of activities in these sectors. These include schemes that subsidize the cost of agricultural inputs and equipment (seeds, fertilizer, tractors etc.), water projects such as dam and construction of smaller works to reduce the risks of drought and recharge aquifers, and production marketing schemes. The AFPPF is financed through a system whereby YR 2.5 (increased from YR1 since 1995) is deposited for every liter of diesel sold in the country. Resources also come from the general budget and foreign grants. The yearly budget is around YR 4.5 billion (US$25 to 27 million).

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The Fund’s role is essentially to appraise, approve and finance projects that are formulated by others namely the agricultural cooperatives, the Agriculture Cooperative Union, the private sector or the local Ministry of Agriculture offices. Once a project is endorsed, these counterparts supervise it, receiving four percent of costs to cover overheads. The AFPPF thus follows a demand-driven format though this is channeled through intermediaries. It is not clear how widespread knowledge about the Fund is or whether all who could productively use it have the opportunity to do so. The terms of financing differ a lot across projects. Some receive full or partial grants, soft loans or loans with subsidized interest rates.

The Fund’s administration and operation has been reviewed elsewhere (El-Gammal et al. 1999).

This evaluation found that the AFPPF did not meet its objectives, in part due to design deficiencies that prevented efficient implementation. Much of the money was found to have gone to parastatals and government entities who probably have access to other forms of credit and to building large dams. Projects were found to be badly designed and formulated. The evaluation may now be out of date and did not address the issue of poverty targeting or impact. Unfortunately, data on participation or a more recent evaluation are not available and so the brief comments below are based on conversations with the Fund’s head.

The program leaders claim that the fund has become more geared to tackling poverty over time. There are currently 34 activities. All are seen to indirectly benefit the poor through employment creation and other benefits, such as increasing the number of crop cycles and the availability of water. Some smaller programs also aim to directly generate income for the poor. Yet, due to a lack of capacity and absorption, expansion of these sorts of programs is limited. One such program emphasizes home-based animal raising. Poor families are given 10 goats or 5 goats and a cow (for a maximum of YR 70,000 per family), taught to raise them and expected to pay back 60% of their value within 2 years. Such schemes entail heavy risks for households who must pay back loans even if the animals die, and so may end up worse off. Another activity encourages the cultivation of date palms by providing credit incentives to farmers to plant trees. Such schemes may be better at reaching poorer households than some of the others (such as building dams) but their cost-effectiveness still needs to be looked at carefully.

Resources are currently allocated to governorates on the basis of population and poverty indicators; governorates where qat is grown are considered rich and are excluded. Within the governorates, the strategy appears to be to focus on one district each year. For the smaller programs, lists of poor people and potential participants are provided by the Sheiks and checked by workers of the local agriculture offices.

One of the objectives of the AFPPF is to make non-collateral based credit more easily available to poor farmers. The only private source of credit in rural areas is the Cooperative Agricultural Credit Bank (CACB) which tends to reach richer farmers and agricultural cooperatives. A program like AFPPF needs to ensure that it doesn’t squander its resources on those who already have access to sources of credit while missing those who don’t and could make productive use of credit. Experience in other countries shows that rich farmers are very adept at cornering the benefits from schemes like the AFPPF and at failing to repay outstanding loans. In making a judgment about this program, more needs to be known about who the direct participants in AFPPF are, what its costs and benefits are and its longer term impacts on poverty. III Donor–Assisted Programs

A large number of international agencies and bilateral donors are active in poverty-related development interventions in Yemen. In common with government programs, many donor-financed

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projects were started in the mid 1990s to mitigate the adverse effects of adjustment and reform. There appears to be increasing cooperation between many of the donors such as in forming partnerships to better meet their objectives. Below, the main donor-financed schemes are described and discussed. There are others that may well have high impacts on poverty, though they are small. Unfortunately, we were not able to cover all donors and interventions. i) The Social Fund for Development (SFD)4

Established in 1997 as a World Bank financed autonomous entity, the SFD was conceived as a

demand driven Social Fund aimed at raising living standards and promoting income earning opportunities for the poor. To meet these objectives it has emphasized community development, capacity building and micro-finance programs in poor areas. The community development activity has largely focused on building small scale infrastructure projects to improve access to education, health and water harvesting services using labor intensive techniques. (Feeder road, environment and cultural heritage micro-projects are also allowed but less popular.) This is complemented by support to NGO, government, private sector and community projects that promote the delivery of services. Income generation is supported through providing micro-credit, savings and income-generating programs to the poor through intermediary institutions. An early program to help banks develop the capacity for lending to small enterprises has been abandoned. The SFD works through partnerships with third parties such as NGO’s, government, cooperatives and other community entities that work closely with communities. This aspect of the project requires the SFD to engage in a fair amount of capacity building geared both to NGOs and CBOs as well as the communities themselves.

As of May 2001, the SFD had provided US$90.3 million to 1,465 projects which it is estimated

have benefited 3.4 million Yemenis. By far and away the most common projects are in the education sector 788 projects with a total commitment of US$46.2 million. Water projects follow at 279 projects and US$17 million committed. On the employment creation side, 9343 permanent jobs and 3,027,097 person days of temporary work were expected from 1997 through 2000. Of those, only 19 percent of the permanent jobs were expected to be filled by women, and even fewer at 0.3 percent of the temporary work days (Republic of Yemen 2000).

The SFD is now in its second phase. Over time, it has capitalized on early experience and altered its activities in various ways. For example, the second project scales up assistance to the particularly vulnerable and disadvantagednotably destitute women, abandoned children and the handicapped. This social protection component promotes informal education and training, support to rehabilitation centers, orphanages and other centers that cater to these groups, child care and literacy training for female prisoners and so on.

The SFD has also attempted to correct for weaknesses in the demand-driven concept of project identification by taking a more active role in targeting marginal groups and the poorest communities who are less well organized or inadequately represented by intermediaries. In these areas, the SFD identifies special needs and works with the communities to address them. Central among these supply-driven special programs are basic education for girls, water harvesting, and integrated development schemes. Sub-districts with the most dismal indicators are first identified. Further targeting is achieved based on village level indicators and probability of an intervention’s success. For example, among the sub-districts where 40 or more percent of the population are dependent on rain water collection, water harvesting

4 Further discussion can be found in World Bank (1997) and (2000). This section draws on the latter documents as

well as interviews with SFD staff in January 2002.

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projects are targeted to the villages where 95 percent do so, and the population is greater than 250. Among the 50 sub-districts with the lowest girls’ school enrolments, four were chosen to receive a girls’ basic education project based on demand, population density and the proximity of secondary level colleges, and hence, expected effectiveness. Schools are built or rehabilitated, female teachers are trained and communities sensitized to the importance of educating girls. These activities may be accompanied by a water project so as to relieve water collection pressures on girls.

In its second phase the SFD is also broadening emphasis from building infrastructure to building the complementary capacity necessary to make a success of the infrastructure. For example, a water harvesting project is always accompanied by efforts to constitute a water user’s association that will ensure sustainability and efficient usage.

Finally the SFD has also augmented its activities in more remote locations and worked to improve the targeting of its interventions. From the start the SFD has aimed to reach poor communities. Under the first project, resources were allocated across governorates based on a formula combining an index of unmet basic needs constructed with data from the 1994 census (with 0.25 weight) and population density (with 0.75 weight). That has been altered in the second project phase. 30 percent of total resources now go to the supply driven special programs and to target socially marginalized groups. The remaining 70 percent are allocated to governorates and districts using an index constructed from data on access to basic needs from the 1994 census and data on income poverty from the 1999 NPS, with equal weight. Section III below provides an assessment of the SFD’s regional targeting performance.

87 to 90% of the budget goes to communities, 5% to overheads and 5 to7% for consultancies, supervision and community capacity building. Importantly, communities are also required to contribute to the sub-projects in the form of materials, labor time or cash. Six regional offices and 85 staff (including drivers, messengers etc) deliver over $20 million a year to 640 locations including extremely remote ones. Past evaluations show that the SFD delivers these services much more cost-effectively than the government or other donor assisted projects.

Due to its excellent reputation and success at delivery and implementation, the SFD is often considered the only institution that can address problems and support activities that have otherwise fallen through the cracks in Yemen. It now addresses a plethora of diverse needs and activities including capacity building and setting up a data information system at the SWF, helping develop a national social protection strategy, the new social protection component and the special supply-driven interventions. It is not clear that all these activities should be taken on board by the SFD. It will need to be careful not to spread itself too thin and to dilute its effectiveness and impact. However, increased attention to better targeting and its new emphasis on supply-driven interventions to those areas it deems the most disadvantaged is applauded.

The 1999 NPS includes a question on whether households have heard of the SFD. Two years after its inception, only 9% of the rural population live in households where a member has heard about the SFD and 13% in urban areas. Thus many more are aware of the SWF. This reinforces the need for renewed information campaigns and the emphasis that the SFD now places on non-demand driven special programs targeted to particularly marginalized areas.

The SFD claims to use labor-intensive techniques for its small-projects building component. However, this appears to get relatively little emphasis. There certainly seems to be scope for reinforcing the labor intensity of the projects and thereby creating employment for the unemployed rural poor.

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The SFD is currently embarking on some rigorous evaluations of the impact of its projects. These will be the first such studies in Yemen and should be of great benefit not only to the SFD’s own work but to that of other poverty programs in Yemen as well. ii) Public Works Project (PWP):

The Public Works Project was established in June 1996 with World Bank funding. It aims to create jobs, provide the poor with small development projects, enhance community participation and develop local contracting firms. By the end of Phase I in June 2000, US$30 million had been spent on demand driven small scale infrastructure projects such as education and health facilities (mostly rehabilitation or extension of existing facilities), water supply or collection, sanitation, road rehabilitation, vocational training and social security. Community contributions totaled an additional US$2.4 million. Under Phase I, 435 sub-projects were completed, of which 54% were education (accounting for 57% of total spending), 18% on health, 11% on water and 9% on roads. The project covers the entire country and about 70 to 75 % of resources are spent in rural areas.

A second project phase began in 1999 and will run to 2003, with $60 million already committed by IDA and the Government. A number of other donors have also come forward with funds to participate in certain sectors in the PWP. Education continues to be the most popular intervention with water overtaking health in second place. Throughout, the works have focused attention on women and children with their highest priority being girls’ schooling and reducing the heavy water collection burden shouldered by women in Yemen.

The PWP is unable to meet demand. Up till now there have been 12,000 requests, though only 1000 could be provided. In principle, the PWP closely coordinates with the Ministries of Health and Education and the SFD, who undertake to equip and operate the units built by the project. The relationship with the Ministry of Health has been difficult. For example, 16 health units built under phase 1 were non-operational for a long time due to the Ministry’s failure to furnish and equip them. On occasion, the PWP also finances the supply of classroom furniture for the schools built, relying on MOE to provide teachers.

PWP funds are allocated across governorates according to a formula which distributes 50 percent according to population, 30 percent according to the poverty headcount (using the 1999 NPS) and the remaining 20 percent according to remoteness. This reflects a change from the first phase allocation which was made by the steering committee based on population and remoteness only. The governorates then allocate funds across districts according to the selection of proposals made by communities.

Communities or local councils are expected to identify their needs and submit proposals to the PWP. They are sometimes aided by local NGOs. The PWP sub-branches also work to seek out the poorest communities. Participation is clearly very dependent on communities knowing about the project. A new approach whereby requests must come from local councils is under discussion. This would help with the common run-ins with local sheiks who often make demands and want to take over the projects. They frequently apply for the contracts themselves, and make trouble for the contractor trying to extract money from him or the people once they are refused. Arrangements can usually be made but 8 projects have been stopped because of disputes with the sheik over placement of projects within the community. Although the PWP is in principle demand driven, exceptions occur when money is accepted from donors who are keen to invest in agency pet projects such as girls’ schools in specific regions.

Proposals are reviewed and subject to selection criteria including a lower limit of 30% labor content, the sector of intervention, costs below $250,000, community participation, provisions for

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sustainability and the promise of improving living standards for the beneficiaries. The project has increasingly emphasized sustainability through requiring community cost sharing and setting up local maintenance and operating committees for the infrastructure. Communities are required to pay a minimum of five percent of estimated sub-project costs upfront in cash or in-kind. The average size of projects is $50,000.

The creation of jobs is said to be the project’s main objective. The PWP was initially meant to be implemented in high unemployment areas (World Bank 2000a). But, unemployment is not considered in the targeting of resources or the selection of projects. Against that, capital intensive projects are rejected, and smaller contractors who lack heavy machinery are said to be favored. Still, only 30% of the cost of each sub-project actually goes to labor and 20% goes to skilled labor contracted from outside the communities and districts (World Bank 2000c). Only 10% thus goes to locally recruited unskilled labor. As of January 2002, over 2.2 million man-days has been created of which 60% were unskilled labor days. School and health units which account for most of the budget, are less labor intensive than other works.

The Bank’s annual review for 2000 argues for reducing the focus on employment generation even

further because it prevents financing equipment and hardware and forces a rejection of many water projects with high investment costs. The review argues that providing access to water improves living conditions (through effects on consumption, production, and time savings) more than employment generation. Similar arguments are made for spending project money on furnishing schools. However, this puts no weight on the potentially important role of the PWP as a short-term safety-net for the poor. By shifting as far as possible toward relatively unskilled labor intensive techniques of production, the PWP would be able to play a more prominent role in dealing with the problem of uninsured risk and transient poverty facing the poor. There may be a tradeoff with longer-term poverty reduction though this can be overstated. Frequently, highly capital intensive techniques are no more efficient in a low wage economy with abundant labor but are chosen for other reasons.

iii) Poverty Alleviation and Employment Program (PAEG)

The UNDP’s PAEG is an ambitious and complex program under implementation since 1998. It has a number of sub-components. A key part concerns setting up data information systems (including the Poverty Information and Monitoring System (PIMS) and the Labor Market Information System (LIMS)) which resulted in the 1999 National Poverty and Labor Force surveys. The objective is to improve poverty monitoring, targeting and policy impact assessment. The PAEG also includes the ongoing creation of a National Committee for Social Safety Net with responsibility for the major poverty alleviation programs in Yemen.

Of greater relevance to the present discussion are three sub-components of the PAEG that are more directly aimed at reaching the poor.

i) The National Programme for Productive Families (NPPF) intends to increase the

employment and income earning potential of poor families, and in particular, deprived women, through vocational training and skills development. Training has focused on literacy and women’s craft work such as sewing, embroidery, etc. There are 41 training centers around the country with some working through local NGOs and cooperatives. These centers are not all functioning well. The staff is often under-trained, markets do not always exists for the goods created and the ability to integrate graduates into the labor market has tended to remain weak. Finally, as there is no follow-up system for judging the impacts on graduates, longer-term impact is unknown.

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ii) The Micro-Start program is a small enterprise and micro-finance development initiative being implemented through four local NGOs, three of which exclusively serve women. Hence, the scheme is heavily focused on women. The four NGOs are also all urban based. Each NGO was given $150,000 of which one third is for operating costs and the rest for starting a revolving fund. Small loans are granted to individuals with prior experience in the area of investment, a good reputation and a guarantee. Past fully paid-up borrowers can take further and larger loans. The focus has been on the sustainability of the revolving fund rather than on small enterprise and livelihood development and sustainability per se. A UNDP evaluation mission deemed the program to have good repayment rates but not a sustainable impact on enterprises. Beneficiaries appear to be poor but far from the poorest.

iii) The third sub-component of relevance is the Regional Development initiative

(RegDev), a community based area development scheme designed to empower local communities to develop and help themselves. ‘Demonstration’ pilot schemes in each of the five major geographical regions involve setting up the institutional framework for community based organizations to flourish, training them in various skills, providing them with social services and technical and financial assistance so that they can engage in income generating and wealth creating small projects. Direct employment generation and livelihood benefits are expected for the entire communities. RegDev is being implemented in partnership with other UN agencies, local NGO’s, WFP and others active in the chosen areas.

iv) The World Food Program (WFP):

The World Food Program has long been active in promoting food security in Yemen. In its latest program (2002-2006), it aims to do so by concentrating attention on women and girls as development change agents and by carefully targeting interventions geographically.

Targeting follows a three stage prioritization. First, the most vulnerable (as measured by FGT poverty indices based on the 1999 NPS) and the most food insecure (using nutrition indicators) districts are identified. Next, a prioritization is made among those according to local needs in the planned WFP areas of intervention such as, for example, MCH, girl’s education etc. The third level of prioritization considers more practical matters such as accessibility, the security situation, and the presence of complementary activities by other organizations. This results in a concentration of WFP activities in 77 districts in 15 governorates.

Forty million US dollars worth of food commodities have been committed so far (50 million more is being sought) to be used to implement the new program’s three components and directly benefit 260,310 beneficiaries in the targeted regions. The first component is nutrition support to malnourished women and children. This is implemented by providing food assistance to pregnant and nursing mothers and children under 5 who are identified by mid-wives as malnourished when they attend health care centers. Chosen beneficiaries are given monthly take-home rations for a set number of months, provided they regularly attend the clinic and stick with the treatment. Within the WFP targeted Districts, health centers are selected for participation based on their ability to provide MCH services and health and nutrition education which is also part of the program.

The second component of WFP’s program is aimed at promoting access to primary education for girls through a food-for-education style scheme. Parents receive a wheat and vegetable oil ration for every three months a daughter attends primary school and for as many daughters as do so. Within the geographically targeted regions, further targeting aims to identify schools in communities with particularly low girls’ enrolments, high levels of female child labor, and the capacity to take in more

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pupils. There is no targeting within a selected school so that all attending girls receive food transfers. These are distributed each quarter by parents’ associations, teachers and head masters with supervision from WFP. In an earlier version of this scheme, the response rate far exceeded WFP’s expectations leading to teacher and school size constraints and insufficient food stocks. To avoid running into the same problems, WFP is now working more closely with the Ministry of Education, the Social Fund, donors, NGOs and local communities to expand and rehabilitate schools so that all girls can be accommodated.

The third smaller activity in WFP’s program is aimed at supporting the economic empowerment of women by using food transfers as incentives for women to participate in skills training, credit and income-generating activities. WFP helps identify micro-projects that help reduce the burdens, such as water and fuel collection, on women’s time. This will be coupled with small food-for-work projects open to both genders but which will create assets that directly benefit women. Here too, WFP is working closely with partners including UNICEF, IFAD, the Dutch and the Social Fund.

Finally, WFP continues to be responsible for assisting close to 20,000 Somali refugees who have been in Yemeni camps for the last 11 years.

At first sight at least, there are many things to commend in WFP’s program of interventions. The focus on nutrition and children is important, given the very high, and possibly rising, rates of child malnutrition in Yemen. Fine targeting to certain particularly disadvantaged districts helps to focus resources. Each intervention also includes a degree of self-targeting whereby participants must bear a cost to receive benefits they must attend clinics and health education briefings, or ensure that girls attend school or provide labor. International experience shows that this helps guarantee that those who do not need the assistance do not participate and hence improves targeting outcomes. It also reduces the administrative costs of identifying participants. Other programs could learn from studying WFP’s approach. However, there is still a need to evaluate cost-effectiveness and impacts on living standards. Without that information, it is difficult to be sure that the interventions are fulfilling their aims.

v) The Southern Governorates Program (SGP)

Former South Yemen implemented a land reform program that was revoked in 1993 at reunification. The SGP was initially designed to compensate small farmers who lost their lands then by allocating them new land. From the beginning this was a politically charged project which eventually found that there was, at any rate, no land to give as well as very limited water in these areas. The SGP was reoriented from targeting the land-dispossessed to targeting the poor. Implemented with IFAD, the project has become more like a demand driven social fund focusing on community development in 40 communities in the provinces of Hadramout, Abyan, Shebwa and Lahej. Capacity is built through a community development fund to help finance small development projects. Efforts are currently underway to restructure the SGP by working through local councils and by emphasizing small agricultural infrastructure projects. IV Assessing the Regional Targeting Performance of Poverty Programs

For many of Yemen’s poverty programs, an analysis of program incidence at household level is not feasible because information on household participation is not available. However, information on cross-governorate budget allocations is available for the SFD, PWP, and SWF. There is also information on district level allocations for the SFD. These data, together with provincial and district level poverty measures, allow an analysis of inter-governorate, and for the SFD intra-governorate, targeting

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performance. Given recent efforts to better target program allocations to poorer regions, it is of interest to examine geographical targeting performance.

Following Ravallion (2000) I estimate the ‘targeting differential’ interpretable as the mean difference in spending between the poor and non-poor. Targeting performance is measured by exploiting the spatial variances in both spending and poverty incidence across governorates. The inter-governorate targeting differential is estimated by regressing program allocation across governorates on the governorate poverty measure (given by the percent of poor households based on the 1999 NPS). This gives a measure of how well program allocations match the governorate poverty map. To estimate the intra-governorate targeting performance for the SFD, I run an OLS regression of program spending on poverty across all districts within each governorate.

Table 11 gives the mean estimated amounts going to poor and non-poor households across governorates and the difference in the two the targeting differential for a number of different programs. The SFD’s first phase allocations across governorates were not pro-poor. The targeting differential is not significantly different from zero. Although it is clear that it wasn’t pro-poor, it is hard to say whether none went to the poor or roughly the same went to the poor as went to the non-poor. Yet, the second phase targeting turns this around completely. Poor households now benefit by about US$90 which is $62 more than the non-poor on average. The SFD’s increased efforts at better geographical targeting have demonstrably paid off.

This does not appear to be the case for the PWP. For both phases of the PWP, one can not reject the null hypothesis that the amounts going to the poor and the non-poor are the same; equally well, one can not reject the null that the poor got nothing. These results suggest that the program allocations across governorates are biased against the poor. No improvement is discernible in the second phase of the program after its targeting criteria were revised. The inter-governorate distribution of SWF allocations shows no signs of pro-poor targeting. Again the amount going to the poor is not significantly different from zero.

These results concern cross-governorate allocations and the extent of pro-poor geographical targeting, but say nothing about how the money was spent within governorates. It is possible that programs are reaching the poor in richer governorates. Analyses of transfer programs in other countries show that inter-regional targeting is often less pro-poor than intra-regional targeting (Alderman 2002, for Albania; Galasso and Ravallion 2002 for Bangladesh).

Next, Table 12 presents the same information for intra-governorate targeting performance for phases I and II of the SFD. This uses data on district level allocations and poverty rates. As noted earlier, in its first phase, resources were targeted according to population density with 75% weight and an index of unmet basic needs with 25% weight. Unfortunately, we do not have that composite index and so can not test the degree to which targeting accorded with it. In its second phase, the SFD targets districts on the basis of poverty and an index of unmet basic needs. A test of the allocations against the actual index used shows practically perfect targeting to those prioritized by the index. However, it is also of interest to ask how well the budget allocations in the two phases were distributed from the point of view of the narrower poverty indicator.

Table 12 shows a very mixed picture for phase I. Out of the 12 governorates for which there are

sufficient district observations to estimate the targeting differential, for 7 the differences between the amounts going to the poor and non-poor are not significantly different from zero, three have negative differentials indicating that the non-poor are favored and only two have significant and positive differentials. Hadramout appears to perform particularly badly in reaching its poor. Poverty targeting performance changes markedly in the case of the second phase allocations. Table 12 reveals consistently

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positive and significant targeting differentials for phase II except for Hadramout and Abyan where the difference in the amounts going to the poor and non-poor can not be considered significantly different from zero. In all governorates, except Mareb, targeting has significantly improved in phase II. However, there is also quite a lot of variance in these targeting differentials across governorates. Some clearly perform much better in reaching their poor districts. In a few cases (Taiz, Aden, Dhamar and Al-Baida) the entire allocation appears to reach the poor. V Overall Assessment and Recommendations:

It has been argued that the SWF looks after those who are unable to work and look after themselves, the PWP provides employment to the able-bodied who can work, while the SFD provides long term development opportunities for the poor and that this makes for a complete safety net program in Yemen (World Bank 2000b). Yet a review of these programs indicates that this is not the reality even when these programs are coupled with other existing poverty and safety net schemes. First, the coverage of these programs is extremely limited compared to the needs and is far from able to work to meet these different needs in the same areas. But even if the programs vastly increased their coverage, they would still not combine to fulfill these aims. The most striking aspect of the programs reviewed is in the similarity of their objectives, format, methods and benefits delivered. The PWP is in many ways indistinguishable from the SFD in its aims and design. With the exception of the SWF, these and practically all other interventions resemble demand-driven social fund type programs where, to varying degrees, the operative emphasis is on ‘providing long-term opportunities for the poor.’

There’s a common focus on women and children, women’s education, community development, building small infrastructure and bringing services to communities. In many ways these efforts are making up for the social services and physical assets that government ministries fail to deliver. Such programs are clearly necessary and many appear to be working well. However, many of the benefits are likely to be longer term. Together the schemes do not fulfill an insurance or traditional safety net role that helps prevent destitution and asset depletion by helping people through shocks and short-term difficulties (World Bank 2001). They may well also fail to reach the currently poorest and most needy. Children, for example, may not be sufficiently well protected from hunger and poverty today, or their lifelong consequences. This is a clear deficiency of Yemen’s current safety net.

In theory, the SWF is an exception. It attempts to provide protection to the worse off among those who are unable to work. Yet, the only program that could conceivably address shocks and prevent permanent destitution is administratively cumbersome, slow to respond and of extremely limited coverage. In addition, the evidence points to extremely weak targeting efficiency of current SWF payments at both governorate and household level. Poverty impacts are also questionable. The SWF should be reformed to better meet these objectives in practice. It would be advisable to develop this instrument (with appropriate design changes) to better reach the non able-bodied poor and be more responsive to idiosyncratic shocks and vulnerability. As discussed, this requires that, together with more stringent geographical targeting to designated poor areas, the identification and targeting of participants be simplified and broadened. The current effort at fine targeting with both income and asset tests is of doubtful value and much too burdensome administratively. Instead the SWF should work to establish indicators on the basis of poverty data that are more easily verified and transparent as well as difficult to manipulate.

Many correlates of poverty will be public knowledge in communities. To better tap into this

knowledge and avoid relying solely on Sheiks, one might think about setting up a local women’s council in targeted communities who would be responsible for identifying the eligible based on set criteria, including ones that identify those who may not normally be poor but are hit by a temporarily or

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permanently debilitating shock. The fact that women figure so highly among the disadvantaged who are unable to work provides an important rationale for relying on them. They are also likely to have intimate knowledge about the living standards of community members. The SWF should also add a school attendance requirement for the school-aged children of recipients as a condition for receiving payments. (Obviously, this can and should only be enforced if a school is accessible.) The women’s council should be required to consult closely with teachers to ensure that children of beneficiaries are attending school. If not, payments should be cut off.

The women’s council’s recommendations would go to the local council or district office where

final decisions on beneficiaries would be made. Following a first stage of fine geographical targeting, more decentralization of this sort for the next stages would be highly desirable and reduce the response time needed to reach beneficiaries. More resources should also be devoted to the SWF. With some redesigning the SWF could perform an extremely valuable role in Yemen’s safety net but the targeted amounts are currently far too small to make much difference to the most needy.

In addition to these changes to the SWF, thought needs to go into how other instruments can be

better developed to serve an insurance role and address vulnerability for the able-bodied poor. Yemen is a country with considerable permanent and seasonal unemployment. Agriculture the source of livelihood for most of the population is subject to harsh environmental conditions, precarious access to water and considerable variability. Households are subject to multiple risks that affect their ability to escape poverty. Many countries have successfully addressed issues of vulnerability and income variability with self-targeted workfare programs. A key player could be the PWP or a new public works program. Either way the scheme would need to increase its self-targeting aspect and be designed to better address vulnerability to seasonal and other income earning and living conditions variability. Again, there should be much better targeting to geographical areas based on levels of unemployment and underdevelopment, as well as targeting on the basis of year round employment variation focusing on the lean periods. It is important to recognize that private assets are key to employment and well-being as does the current PWP. But, public works can also better serve the short-term employment and consumption smoothing function. The program must be willing to face some tradeoffs, for example by building assets that are not as good or sustainable as they might be if less of the project funds went to employment generation. Other programs such as the SFD appear to be going in the right direction with its increased focus on better targeting and its attempts to reach the most disadvantaged areas. However, it could do more to increase the labor intensity of its small-scale infrastructure projects.

A final focus that appears to be missing from Yemen’s current safety net has to do with protecting children. Other than efforts at improving schooling, particularly for girls, current programs appear to place little emphasis on child nutrition and other conditions that can cause irreversible damage. The WFP’s programs provide an exception, and thought could go into replicating and expanding on some of their health and nutrition and food for education schemes that aim to protect children from the future consequences of current poverty.

It is gratifying to see the extensive use of household survey poverty data for targeting funding

allocations across programs. The SFD also makes good use of census data to target more finely geographically. In theory, this should be further encouraged. However, current practice needs to be vastly improved. As discussed, geographical budget allocations are not pro-poor for most programs for which we have the information. The SFD’s phase II is the big exception. The other schemes’ targeting criteria clearly need to be revised. It will also be important for the next household survey to focus on collecting a robust measure of expenditures so that the currently used poverty numbers can be checked. The programs should then use the updated regional poverty numbers (adjusted for inflation) to retarget allocations. It will be extremely important to ensure that the next household survey collects the necessary data for effective targeting, as well as information that allows analysis of program participation and incidence.

19

The criteria currently used to target funds geographically appear questionable in some cases. This may explain the lack of pro-poor targeting revealed above. For example, most programs use population density or levels as a criteria, along with poverty rates. This makes little sense: the population weight may simply cancel out the weight given to poverty. It would be better to make per capita allocations a function of the poverty rate only. In addition, there may be a ‘fixed cost’ argument for allocating more to certain regions, where due to difficult terrain, remoteness and so on, interventions simply cost more. The solution then is to make the total allocation proportional to the total number of poor as long as that allocation does not fall below some amount which represents the fixed cost. This would also do away with the need to use ‘remoteness’ as a targeting criteria.

There is clearly poor representation at local level. One recent important change in Yemen is the

election of local councils. Many of the programs discussed above are aiming to rely and work more closely with the councils. This is a promising direction. However, it will be vital to monitor these developments and evaluate their role in reaching the poor and additional potential for using them or regulating them. Recent moves towards greater decentralization and local council elections may lead to more effective service delivery but also more local capture.

Another promising avenue would be to make better use of radio for advertising demand driven

programs and the SWF. A relatively large percentage of the rural population (51%) has access to a radio according to the NPS, though only 31% of those in the lowest decile. There are also pronounced variance across states with 71 percent of Hadramout’s rural population having a radio compared to only 31 percent of Al-Hodeidah. Televisions are much less common.

Many more impact evaluations and cost-effectiveness studies need to be completed before

definitive assessment of the existing transfer programs and recommendations for reform can be made with reasonable certainty. The SFD has taken the lead here, and it is hoped that other programs will follow suit. We recommend that much more emphasis be placed on understanding the costs and benefits of the transfer programs to poor people.

20

References Alderman, Harold, 2002, “Do Local Officials Know Something We Don’t? Decentralization of Targeted Transfers in Albania,” Journal of Public Economics 83: 375-404. El-Gammal, Yasser, S.R. Katariya and Piet Goovaerts, 1999, Report on Review of the Agriculture and Fisheries Production Promotion Fund, Republic of Yemen, World Bank Galasso, Emanuela and Martin Ravallion, 2002, “Decentralized Targeting of an Anti-Poverty Program,” Journal of Public Economics, forthcoming. Jalan, Jyotsna and Martin Ravallion, 2002, “Estimating the Benefit Incidence of an Antipoverty Program by Propensity Score Matching,” Journal of Business & Economic Statistics, forthcoming. Ravallion, Martin, 2000, “Monitoring Targeting Performance When Decentralized Allocations to the Poor are Unobserved,” World Bank Economic Review 14(2): 331-45. Republic of Yemen, 2001, “Public Works Project: June 1996 to September 2001,” Sana’a, Republic of Yemen. Republic of Yemen, 2000, “Social Fund for Development: 2000 Annual Report,” Sana’a, Republic of Yemen. UNDP, 2001, “Final mission report: Poverty Alleviation & Employment Generation Programme, mid-term evaluation, Sana’a, Republic of Yemen. UNDP, 1998, “An Operational Strategy for Community-Based Regional Development in the Republic of Yemen: Poverty Alleviation & Employment Generation Programme,” Sana’a, Republic of Yemen, March. van de Walle, Dominique, 2002, “The Static and Dynamic Incidence of Viet Nam's Public Safety Net,” Policy Research Working Paper No. 2791 Development Research Group, World Bank, Washington, D.C., February 2002. van de Walle, Dominique, Martin Ravallion and Madhur Gautam, 1994, "How Well Does the Social Safety Net Work? The Incidence of Cash Benefits in Hungary 1987-89," Living Standards Measurement Study Working Papers, 102, 1994.\ World Bank, 2001, Social Protection Sector Strategy: From Safety Nets to Spring Board, World Bank, Washington, D.C. World Bank, 2000a, Implementation Completion Report: Public Works project, December, report no. 21074, World Bank, Washington, D.C. World Bank, 2000b, “Project Appraisal Document on a proposed credit in an amount of SDR 56 million to the Republic of Yemen for a Second Social Fund for Development Project,” Report No. 20291 YEM, World Bank, Washington, D.C. World Bank, 2000c, Public Works Project 1 & 2: Annual Review, May, World Bank, Washington, D.C.

21

World Bank, 2000d, “Republic of Yemen, Comprehensive Development Review, Phase I: Poverty and Social Safety Nets Building Block,” January, World Bank, Washington, D.C. World Bank, 1997a, “Project Appraisal Document on a proposed IDA credit in an amount of SDR 21.7 million to the Republic of Yemen for a Social Fund for Development Project,” Report No., 16301 YEM, World Bank, 1997b, “Yemen: Social Welfare Fund: Assessment and Recommendations,” World Bank, Washington, D.C.

22

Table 1: Poverty and safety net programs in Yemen Public sector Intended beneficiaries Form of benefit Poverty targeting Diesel subsidies Social Welfare Fund War Veterans Fund Tribal Authorities Fund Agriculture and Fisheries Production Promotion Fund Social Fund for Development Public Works Project World Food Program Poverty Alleviation Program Southern Governorates Project Pension schemes

Not clear The poor & unable to work w/out income sources & their dependents. 1962 war veterans. Tribal groups Poor farmers, pastoralists, & fishermen. Poor communities; girls & women; vulnerable & disadvantaged women & children. Poor communities; the unemployed; girls & women. Girls & women Women; poor communities. Rural poor in Southern governorates Retired contributors & employees of army, police & gov’t

Diesel fuel consumption subsidy Cash transfers Cash transfers Cash transfers Ag production promotion; lower input prices; employ’t creation. Community developm’t; facilities/infrastructure assets; social services; credit; capacity building/ training. Employm’t; facilities/infrastructure assets. Food transfers; training, credit, income generation, infrastructure assets. Credit, training, enterprise promotion; community developm’t. Community developm’t; ag infra assets. Cash payments

No Geographical; means-test + status indicator No n.a. Geographical; Geographical; Geographical; work requirem’t. Geographical; clinic/school attendance or work requirem’t Geographical Geographical no

Private sector Remittances from relatives working abroad Remittances from relatives in Yemen (transfers to dependents) Religious charity donations (Zakat & Sataqa) Other traditional community and kinship-based systems and organizations National non-governmental charitable associations

23

Table 2: Distribution of net public and private transfers in 1998 under different assumptions about the propensity to consume out of transfers (annual YR per capita). Welfare indicator:

Per capita expenditures net of transfers Per capita expenditures net of 0.5* transfers Per capita expenditures with tranfers fully included

net mean per capita transfers net mean per capita transfers net mean per capita transfers 1998 National deciles

Rural Urban National Rural Urban National Rural Urban National

1

14757

32942

17347

8879

15924

9707

1181

1651

1233

2 3169 5482 3552 2292 4242 2618 1625 2055 1696 3 2290 4165 2671 2796 4203 3088 1650 2468 1818 4 2158 3925 2528 2639 3519 2826 2331 2311 2327 5 2237 2718 2346 2012 3323 2293 1985 3200 2252 6 985 2601 1352 2353 3524 2620 3246 3693 3350 7 1777 3153 2106 2994 4004 3248 3039 4658 3443 8 1294 3172 1780 2453 4227 2914 5138 4948 5090 9 1475 3987 2146 2766 5167 3419 4860 6400 5288 10 1749 2023 1851 3698 6952 4933 10777 11915 11217

Total 3358 5139 3770 3358 5139 3770 3358 5139 3770 Source: 1998 HBS Note: Deciles are formed by ranking the population by household per capita expenditures under different assumptions about the propensity to consume out of transfers. Net transfers are calculated from income and expenditure on household transfers that can be identified in the HBS—namely, income from zakat, retirement and pensions, local and foreign remittances and payments from government organizations minus transfers given on Zakat, aid to dependents and other gifts and donations. The total household expenditure variable includes expenditures on transfers, so that only transfer income needs to be netted out to get at the “net” amounts.

24

Table 3: Percent of population living in households who received public and private transfers in 1998 Welfare indicator:

Per capita expenditures net of transfers Per capita expenditures net of 0.5* transfers Per capita expenditures with tranfers fully included

% of population in households who received transfers

% of population in households who received transfers

% of population in households who received transfers

1998 National deciles

Rural Urban National Rural Urban National Rural Urban National

1

57.1

80.3

60.4

43.8

65.6

46.4

30.5

51.1

32.8

2 34.1 63.5 39.0 29.8 59.5 34.8 30.4 52.0 34.0 3 30.7 55.7 35.8 30.8 54.5 35.7 25.7 53.0 31.3 4 25.1 53.5 31.1 26.8 51.4 32.1 27.5 47.9 31.7 5 22.6 47.4 28.2 23.6 48.3 28.9 22.8 49.7 28.7 6 18.8 46.7 25.1 23.1 48.7 28.9 28.9 49.3 33.7 7 25.8 42.2 29.7 28.2 44.2 32.2 27.8 46.7 32.5 8 21.2 45.7 27.5 24.6 48.6 30.9 29.1 49.9 34.4 9 22.3 40.5 27.1 25.7 42.5 30.3 31.5 45.9 35.5 10 19.4 38.1 26.3 22.8 42.2 30.2 29.0 46.8 35.8

Total 28.3 48.8 33.0 28. 3 48.8 33.0 28.3 48.8 33.0 Source: 1998 HBS Note: Deciles are formed by ranking the population by household per capita expenditures under different assumptions about the propensity to consume out of transfers. Net transfers are calculated from income and expenditure on household transfers that can be identified in the HBS—namely, income from zakat, retirement and pensions, local and foreign remittances and payments from government organizations minus transfers given on Zakat, aid to dependents and other gifts donations. The total household expenditure variable includes expenditures on transfers, so that only transfer income needs to be netted out to get at the “net” amounts.

25

Table 4: Public and private transfers as a share of household expenditures Net national population deciles

Transfers as a percentage of total household expenditures

Rural

Urban

National

1

35.1

56.0

38.1

2 8.3 12.9 9.0 3 5.8 8.8 6.4 4 4.4 7.8 5.1 5 4.0 5.2 4.3 6 2.2 4.7 2.8 7 3.2 4.6 3.5 8 2.2 4.2 2.7 9 2.0 4.2 2.6 10 1.6 2.7 2.0

total 7.3 8.7 7.7 Source: 1998 HBS. Note: Transfers include income from zakat, retirement and pensions, local and foreign remittances and payments from government organizations.

26

Table 5: Incidence of the percentage of individual transfers in total public and private transfer income in 1998 (YR per year per capita) National population

Total transfers

Zakat Local remittances & other gov’t orgs

Foreign remittances

Retirement & pension

Net decile % of total % of total % of total % of total

1 17520 3.0 26.8 63.6 6.6 2 3688 7.9 28.8 52.2 11.1 3 2816 6.5 34.2 48.8 10.5 4 2752 6.8 35.6 47.3 10.3 5 2659 7.6 30.7 48.9 12.9 6 1747 12.5 42.2 31.0 14.4 7 2532 9.0 34.1 45.2 11.7 8 2207 9.4 34.5 42.4 13.7 9 2745 8.6 33.7 46.7 11.0 10 3557 7.4 36.9 45.7 9.9 total 4224 6.0 31.1 53.5 9.5 Rural 1 14909 3.2 26.0 65.7 5.2 2 3314 6.8 31.3 53.8 8.1 3 2445 4.0 36.9 53.9 5.2 4 2381 4.9 39.6 52.1 3.5 5 2574 5.4 31.7 54.7 8.2 6 1414 12.4 47.0 30.1 10.5 7 2235 7.5 32.2 52.0 8.4 8 1739 7.4 37.5 46.3 8.7 9 2036 8.7 35.0 50.1 6.1 10 2699 5.9 39.6 48.3 6.1 total 3726 5.1 31.5 57.2 6.2 Urban 1 33238 2.6 28.9 58.0 10.5 2 5570 11.3 21.3 47.4 20.0 3 4268 11.9 28.3 37.4 22.4 4 4148 11.1 27.0 37.0 24.9 5 2951 14.1 27.7 31.5 26.7 6 2882 12.5 34.2 32.4 20.8 7 3478 12.2 38.0 31.3 18.5 8 3547 12.2 30.4 36.7 20.7 9 4689 8.3 32.3 42.6 16.9 10 5013 8.9 34.3 43.4 13.4 total 5879 8.0 30.1 45.6 16.3 Source: 1998 HBS Note: Individuals are ranked into national population deciles based on household per capita expenditures net of transfers receipts .

27

Table 6: Incidence of transfer incomes (% of population) National population

% of population living in households who received:

Net decile Zakat charity

Local remittances

Foreign remittances

Retirement and

pension

1 22.2 35.2 20.9 7.2 2 15.7 20.5 8.4 5.0 3 14.9 17.9 9.1 2.9 4 12.4 16.5 7.1 2.7 5 11.4 13.6 5.7 3.1 6 10.7 13.9 3.2 2.7 7 10.4 17.5 6.7 3.2 8 10.8 15.1 6.0 3.2 9 9.1 15.1 6.4 3.4 10 7.8 15.5 5.9 2.8 total 12.6 18.1 7.9 3.6 Rural Net decile 1 20.1 35.5 20.1 4.8 2 10.2 21.4 8.4 3.3 3 9.5 17.9 9.6 1.5 4 7.2 15.6 7.1 1.1 5 6.1 13.0 5.7 1.8 6 5.9 12.6 2.4 1.6 7 6.5 17.2 7.2 2.4 8 6.2 12.7 6.1 1.9 9 5.1 13.6 6.3 2.7 10 3.6 12.6 5.2 1.8 total 8.3 17.6 8.0 2.3 Urban Net decile 1 35.2 33.1 26.2 21.8 2 43.3 16.1 8.0 13.5 3 36.0 17.9 7.2 8.3 4 32.0 19.7 7.0 8.6 5 29.8 15.6 5.6 7.3 6 26.9 18.3 5.8 6.6 7 23.0 18.7 5.3 6.0 8 24.1 22.1 5.9 7.1 9 19.9 19.0 6.9 5.5 10 15.0 20.3 7.1 4.5 total 26.7 19.8 7.7 8.0 Source: 1998 HBS. Note: Individuals are ranked into national population deciles based on household per capita expenditures net of transfers receipts.

28

Table 7: Estimated SWF target population and coverage in 1999 (%) Target Non-Targeted Poor Others SWF yes

0.88

0.33

0.84

2.05

SWF no

20.04

18.76

59.15

97.95

total 20.91 19.10

59.99

100.00

Source: NPS 1999 Note: We define the target group as the very poor (as defined as those in the lowest decile net of SWF payents); the population living in households with a severely disabled adult, an elderly man or woman, and headed by a widowed, divorced or never married woman and who are poor (as defined as being below the poverty line). The above is a table of individual level obs (n=368001) with sample-weighted percentages for belonging to a household with at least one member receiving SWF transfers against belonging to a target/non-target household. A household is defined as ‘targeted’ by the program if the per capita expenditures net of SWF payments place it in the bottom decile, or if it is in deciles 2-4 and has one or more of the following members: a disabled adult beyond school age; an elderly man (over 60) or woman (over 55); a widowed, divorced, married or never married female head (in a household) that is not receiving income from pension and insurance, private domestic remittances or private external remittances. Of the non-target group, the poor are those in deciles 2 to 4, and all else are in 'other.' Table 8: Incidence of SWF payments in 1999 ( YR per year per capita and % of population) 1999 net national deciles

Mean per capita SWF transfers % of population in households receiving SWF

Rural Urban National Rural Urban National 1

340

930

449

4.2

8.9

5.1

2 78 143 92 2.2 3.8 2.6 3 68 61 66 2.1 2.3 2.1 4 128 71 114 2.2 2.2 2.2 5 41 82 51 1.6 1.9 1.6 6 46 57 49 1.5 2.3 1.7 7 53 54 53 1.3 1.7 1.4 8 34 37 35 1.4 1.1 1.3 9 52 37 47 1.3 1.2 1.2 10 115 46 89 1.3 0.8 1.1

total 98 124 105 1.9 2.3 2.0 Source: 1999 NPS. Note: Deciles are defined based on per capita household expenditures net of social welfare fund payments.

29

Table 9: Percent of population with amenities and living in an area with various services in 1999 Rural Urban Total % pop with radio 51.4 71.1 56.6 % pop with TV 19.3 80.3 35.3 % pop living in area with: post office 4.8 56.2 18.2 Bank 3.1 47.2 14.6 basic education school 77.8 91.2 81.3 secondary school 36.5 81.7 48.3 primary health care center 23.8 67.1 35.1 hospital 7.6 61.9 21.8 public transportation 10.9 73.3 27.2 cooperative association 6.6 47.4 17.3 Source: 1999 NPS

30

Table 10: Population possibly consuming diesel through use of generators for lighting or irrigation in 1999 National population deciles

% population with electric generator

% population with private generator as main lighting source

at home

% population who irrigate with artesian well

total % population who may use diesel

rural urban rural urban rural urban rural urban 1

2.2

1.2

1.1

0.3

4.1

0.7

6.4

2.0

2 2.2 2.1 0.9 0.1 5.4 1.2 7.7 3.4 3 2.8 3.2 1.5 0.2 6.5 1.4 9.2 4.6 4 4.4 1.7 2.2 0.2 7.6 1.8 11.9 3.5 5 3.6 3.6 2.1 0.2 7.9 2.5 11.1 6.0 6 4.0 3.6 2.3 0.3 9.2 1.9 13.0 5.2 7 4.0 2.6 1.8 0.4 12.2 3.2 15.8 5.7 8 6.0 4.5 3.5 0.6 12.4 3.1 17.8 7.6 9 7.0 4.9 4.5 0.4 15.5 3.5 21.9 8.3 10 11.0 8.5 5.3 0.7 19.5 5.8

28.1 13.4

total 4.6 3.9 2.4 0.4 9.7 2.8 13.9 6.5 Source: 1999 NPS Note: Deciles are defined based on total household per capita expenditures.

31

Table 11: Program performance in targeting the poor across governorates Actual mean per

h’hold allocation Estimated

mean amount going to poor

Estimated mean amount going to

non-poor

Estimated targeting differential

SFD phase I ($US/per household)

32.7

-66.8 (1.0)

81.2 (2.2)

-148.0 (1.4)

SFD phase II ($US/per household)

47.9 90.2 (6.1)

28.2 (3.6)

62.0 (2.8)

PWP phase I ($US/per household)

12.9 -19.8 (0.8)

28.2 (2.0)

-48.0 (1.2)

PWP phase II ($US/per household)

33.8 -62.8 (0.6)

79.1 (1.4)

-141.9 (0.9)

SWF (YR/per household/yr)

5060.9 -8831.4 (0.8)

11566.9 (1.8)

-20398.3 (1.2)

Note: T-ratios in parentheses are based on standard errors corrected for heteroskedasticity. The targeting differential is the difference between the per household amounts going to the poor minus that going to the non-poor. When an amount is not significantly different from zero, it is set to zero when calculating the targeting differential.

32

Table 12: The SFD’s performance in targeting the poor across governorates under phases I and II ($US/per household)

Mean per h’hold allocation to governorate

Mean amount going to poor

Mean amount going to non-poor

Targeting differential

Phase I Phase II Phase I Phase II Phase I Phase II Phase I Phase II Ibb

22.1

51.0

14.2 (4.9)

100.5 (19.9)

26.8

(12.1)

29.0

(11.9)

-12.6 (2.6)

71.5

(10.0) Abyan 33.4 48.7 -- 71.0

(2.2) -- 41.9

(3.3) -- 29.1

(0.7) Sana’a City 20.5 34.3 -- 90.7

(11.2) -- 6.8

(2.9) -- 83.8

(8.4) Al-Baida 32.6 54.8 73.7

(3.7) 121.9 (20.4)

-0.03 (0.0)

6.3 (0.6)

73.7 (2.1)

115.6 (7.3)

Taiz 19.8 52.2 27.4 (2.2)

108.7 (8.7)

25.9 (3.3)

16.8 (1.4)

1.6 (0.1)

91.9 (3.9)

Al-Jawf 20.3 55.7 -- 94.8 (21.8)

-- 31.4 (8.1)

-- 63.4 (7.9)

Hajja 22.5 57.2 16.6 (5.5)

93.9 (48.0)

23.2 (17.2)

34.8 (21.3)

-6.6 (1.7)

59.1 (19.4)

Al-Hodeidah 18.3 52.8 539.1 (1.2)

88.1 (17.7)

-212.9 (1.0)

38.0 (10.8)

752.0 (1.1)

50.1 (6.7)

Hadramout 23.5 51.2 -251.4 (2.2)

35.3 (2.2)

316.4 (2.8)

77.9 (5.7)

-567.8 (2.5)

-42.6 (1.4)

Dhamar 22.2 49.5 16.8 (0.8)

140.7 (5.6)

27.3 (3.2)

13.9 (1.1)

-10.6 (0.4)

126.8 (3.3)

Shabwah 35.5 50.4 -- 101.6 (9.2)

-- 23.9 (5.6)

-- 77.7 (5.2)

Sa’adah 29.5 50.6 51.8 (4.3)

86.8 (17.6)

24.8 (5.3)

39.2 (12.9)

27.1 (1.7)

47.5 (6.3)

Sana’a 37.0 50.1 15.0 (2.8)

105.1 (21.4)

25.4 (10.7)

26.9 (8.1)

-10.4 (1.5)

78.2 (9.9)

Aden 24.1 32.7 -- 108.1 (56.3)

-- -3.08 (1.7)

-- 111.1 (31.8)

Lahj 26.3 50.2 -- 90.9 (7.0)

-- 36.2 (11.4)

-- 54.7 (3.4)

Mareb 47.6 45.5 151.3 (4.3)

105.1 (8.9)

30.1 (4.1)

33.6 (6.1)

121.2 (3.0)

71.5 (4.2)

Al-Mahwit 31.0 50.6 2.2 (0.3)

104.1 (8.3)

43.1 (15.6)

28.9 (7.5)

-40.9 (4.6)

75.2 (4.6)

Al-Mahrah

122.4 34.7 -- -- -- -- -- --

Amran -- 47.0 16.5 (1.4)

111.4 (5.1)

25.3 (7.7)

27.7 (3.4)

-8.8 (0.6)

83.7 (2.8)

Dhaleh -- 47.5 -- 78.6 (8.4)

-- 37.1 (24.6)

-- 41.5 (3.8)

Yemen Republic

37.9 51.4 87.7 (1.4)

94.3 (36.5)

13.3 (0.6)

29.7 (21.6)

74.4 (0.9)

64.7 (17.3)

Note: T-ratios in parentheses are based on standard errors corrected for heteroskedasticity. The targeting differential is the difference between the per household amounts going to the poor minus that going to the non-poor. When an amount is not significantly different from zero, it is set to zero when calculating the targeting differential.

MENA Working Paper Series

No. 1 Has Labor Migration Promoted Economic Integration in the Middle East? June 1992. Nemat Shafik, The World Bank and Georgetown University.

No. 2 The Welfare Effects of Oil Booms in a Prototypical Small Gulf State. September 1992. Ahmed Al-Mutuwa, United Arab Emirates University and

John T. Cuddington, Georgetown University. No. 3 Economic and Social Development in the Middle East and North Africa. October 1992. Ishac Diwan and Lyn Squire, The World Bank. No. 4 The Link Between Trade Liberalization and Multi-Factor Productivity: The Case of Morocco. February 1993. Mona Haddad, The World Bank. No. 5 Labor Markets in the Middle East and North Africa. February 1993. Christopher A. Pissarides, The London School of Economics and Political Science. No. 6 International Competitiveness of the Private Industry and the Constraints

to its Development: The Case of Morocco. June 1993. Hamid Alavi, The World Bank.

No. 7 An Extended RMSM-X Model for Egypt: Quantifications of Market-Oriented

Reforms. September 1993. Karsten Nimb Pedersen, The World Bank.

No. 8 A Report on the Egyptian Tax System. October 1993. Mark Gersovitz, Roger H. Gordon and Joel Slemrod, The World Bank. No. 9 Economic Development and Cooperation in the Middle East and North

Africa. November 1993. Ishac Diwan and Lyn Squire, The World Bank. No. 10 External Finance in the Middle East: Trends and Prospects. December 1993.

Ishac Diwan, John Underwood and Lyn Squire, The World Bank. No. 11 Tax Incidence on Agriculture in Morocco (1985-1989). April 1994. Jean-Paul Azam, CERDI, University of Auvergne, Clermont-Ferrand (France)

et CSAE, Oxford (U.K.). No. 12 The Demographic Dimensions of Poverty in Jordan. August 1994. Chantal Worzala, The World Bank. No. 13 Fertility and Family Planning in Iran. November 1994. Rodolfo A. Bulatao and

Gail Richardson, The World Bank. No. 14 Investment Efficiency, Human Capital & Migration A Productivity Analysis

of the Jordanian Economy. May 1995. Gaston Gelos, Yale University, Department of Economics.

No. 15 Tax Effects on Investment in Morocco. August 1995. David Sewell, Thomas Tsiopoulos and Jack Mintz, The World Bank.

No. 16 Reconstruction in Lebanon: Challenges for Macroeconomic Management. April 1999. Daniela Gressani and John Page, The World Bank.

No. 17 Towards a Virtuous Circle: A Nutrition Review of the Middle East and North Africa. August 1999. Regional HNP Knowledge Management, The World Bank. No. 18 Has Education Had a Growth Payoff in the MENA Region? December 1999.

Lant Pritchett, The World Bank. No. 19 Rationalizing Public Sector Employment in the MENA Region.

December 2000. Elizabeth Ruppert Bulmer, The World Bank.

No. 20 Achieving Faster Economic Growth in Tunisia. March 2001. Auguste T. Kouamé, The World Bank.

No. 21 Trade Options for the Palestinian Economy: Some Orders of Magnitude.

March 2001. Claus Astrup and Sébastien Dessus, The World Bank. No. 22 Human Capital and Growth: The Recovered Role of Educational Systems.

April 2001. Sébastien Dessus, The World Bank. No. 23 Governance And The Business Environment In West Bank/Gaza

May 2001. David Sewell, The World Bank.

No. 24 The Impact of Future Labor Policy Options on the Palestinian Labor Market June 2001. Elizabeth Ruppert Bulmer, The World Bank.

No. 25 Reform and Elusive Growth in the Middle-East – What Has Happened in the 1990s? July 2002. Dipak Dasgupta, Jennifer Keller, T.G. Srinivasan, The World Bank.

No. 26 Risks and Macro-Economic Impacts of HIV-AIDS in the Middle East and North Africa: Why waiting to intervene can be costly. July 2002. David A. Robalino, Carol Jenkins, Karim El Maroufi, The World Bank.

No. 27 Exchange Rate Regime and Competitiveness of Manufactured Exports: The Case of MENA Countries. August 2002. Mustapha Kamel Nabli,

Marie-Ange Véganzonès-Varoudakis, The World Bank.

No. 28 Governance and the Investment Climate in Yemen September 2002. Arup Banerji, Caralee McLiesh, The World Bank.

No. 29 Exporting Labor or Goods? Long-term Implications for the Palestinian Economy October 2002. Claus Astrup, Sébastien Dessus, The World Bank.