DEVELOPMENT OF SOCIAL PROTECTION
Pre-feasibility and costing analysis of three short-term social protection
reform options for UNICEF Mozambique
Luca Pellerano
October 2010
Pre-feasibility and costing analysis of three short-term social protection reform options for
UNICEF Mozambique
Acknowledgements
The research team would like to thank the UNICEF and ILO Country Offices in Mozambique, in
particular Lisa Kurbiel, Theresa Kilbane, Nuno Cunha, Karin de Rooij and Maaike Arts for their support
and advice during the course of this study. Their help was crucial for orienting the study, obtaining
documentation and data, and arranging meetings with a wide range of actors engaged in the field of
social protection in Mozambique. While we cannot name all these interlocutors here individually, we
acknowledge their valuable inputs to our research, while absolving them of any responsibility for the
views expressed in this strategy paper or for any errors in the information and data presented.
Luca Pellerano, October 2010
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October 2010
Pre-feasibility and costing analysis of three short-term social protection reform options for
UNICEF Mozambique
Contents
Acknowledgements
i
Abbreviations
iv
1
Introduction
5
2
Overview of results
7
3
3.1
3.2
3.3
PSA full inclusion of children of secondary beneficiaries
Design Considerations
Evidence
Cost simulation
11
11
12
17
4
Grant for children receiving nutrition support
4.1
Design considerations
4.2
Evidence on existing nutrition support interventions
4.3
Cost simulation
24
24
26
28
5
Grant for child headed households
5.1
Design considerations
5.2
Evidence
5.3
Cost simulation
30
30
31
33
Bibliography
35
Annex A
Detailed methodology for PSA long term cost projections
36
Annex B
Macroeconomic and public finance framework
38
iii
October 2010
Abbreviations
ART
ATPU
CO
CRC
CSB
CSO
DPMAS
DFID
DHS
ENSSB
HIV/AIDS
IAF
INAS
INE
MICS
MISAU
MMAS
MT
NGO
OPM
PASD
PES
Plumpy’nut
PGR
PSA
TORs
UNICEF
WFP
Anti-retroviral treatment
Ready to use therapeutic food (Alimento Terapêutico Pronto para Uso)
Country Office (of UNICEF)
Convention on the Rights of the Child
Corn-soya blend
Civil society organisation
Direcção Provincial da Mulher e da Acção Social (Provincial Directorate for
Women and Social Action)
Department for International Development (of the United Kingdom)
Demographic and Health Survey (1997 and 2003)
Estratégia Nacional de Segurança Social Básica (National Basic Social
Security Strategy) 2010-2014
Human Immunodeficiency Virus/Acquired Immune Deficiency Syndrome
Inquérito aos Agregados Familiares sobre Orçamento Familiar (Household
Budget Survey) 2002/03
Instituto Nacional de Accão Social (National Institute for Social Action)
Instituto Nacional de Estatística
Multiple Indicator Cluster Survey (2008)
Ministério da Saúde (Ministry of Health)
Ministério da Mulher e da Acção Social (Ministry for Women and Social
Action)
Metical
Non-governmental organisation(s)
Oxford Policy Management
Programa de Apoio Social Directo (Direct Social Support Programme)
Plano Económico e Social (Economic and Social Plan, annual macro-level
planning instrument)
Ready to use therapeutic food
Programa de Geração de Rendimentos (Income Generation Programme)
Programa de Subsídio de Alimentos (Food Subsidy Programme), of INAS
Terms of Reference
United Nations Children’s Fund
World Food Programme
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Pre-feasibility and costing analysis of three short-term social protection reform options for
UNICEF Mozambique
1
Introduction
OPM was commissioned by UNICEF to produce a new internal UNICEF strategy paper on social
protection programmes grounded in evidence-based research to re-ignite advocacy efforts with the
Government of Mozambique for expansion of social protection programmes.’ The main objective of
the project is addressed by the strategy paper (Hodges and Pellerano, 2010), which is intended to
guide the UNICEF Country Office (CO) in its engagement in the field of social protection.
The ToRs also required a ‘complementary cost-benefit analysis’ to accompany the strategy, ‘by linking
any suggested programmatic responses to required resources’. In subsequent discussions between
the research team and the UNICEF CO, it was agreed that the strategy paper would identify the main
programmatic options to be selected for detailed quantitative analysis of benefits (outcomes and
impacts) and of costs and fiscal affordability. This would ideally imply a sequencing of the activities,
beginning with the strategy paper and then moving on to the cost-benefit analysis. However, the
evolution of the policy-making context in the country, in particular the development of an operational
plan for the implementation of the Government’s basic social protection strategy in mid-2010, led to a
decision to fast-track some options for cost-benefit analysis in tandem with the drafting of the strategy
paper.
In this document we present the costing analysis of three options for social protection reform in
Mozambique, notably: 1) the full inclusion of children as secondary beneficiaries of the Programa de
Subsídio de Alimentos (PSA), the major social cash transfer currently in place in Mozambique; 2) a
new grant scheme for malnourished children receiving nutrition supplementation, and 3) a new grant
scheme for child-headed households.
These policy instruments were identified during the inception mission by UNICEF, government and
non-government stakeholders as possible options for broadening and strengthening social protection
in the short run. It was therefore proposed to undertake a detailed costing exercise, which would
inform on-going planning exercises within the government agencies as well as UNICEF advocacy and
technical assistance in the relevant sectors.
The analysis of the three options required some degree of refinement of the nature of the intervention
intended, particularly with respect to the target group and the value and periodicity of the transfer.
More broadly, it involved a preliminary consideration of issues concerning the relevance, opportunity,
operational feasibility and political viability of the proposed options. The solutions proposed in the
document have been identified on the basis of conversations with UNICEF technical experts and the
competent government offices, mainly the Instituto Nacional de Acção Social (INAS) and, for the grant
scheme for malnourished children, the Ministry of Health (MISAU).
A broader range of implementation and institutional issues will have to be considered in more detailed
feasibility studies if it is decided to pursue any of the options discussed.
A simple model for the costing of the proposed three schemes was developed on the basis of
available administrative and survey data, and population projections. This should provide a rough
indication of the fiscal implications of the three options, so as to orient planning and the decision
making process both in UNICEF and in the Government, particularly at INAS. The costing exercise
includes simple estimates of short term cost implications, as well as medium term cost projections for
2010-2014.
Due to limited access to the 2007 census data, the simulations made use of available information from
other nationally representative surveys - mainly the Household Budget Survey (IAF) for 2002/2003
and the Multiple Indicator Cluster Survey (MICS) for 2008 - as well as administrative records for
existing programmes and official INE population projections based on the previous census (1997).
The dataset recently collected for the impact evaluation of the PSA evaluation has also been
analyzed. While the focus of this document is rather specific, the possibility to directly access and
5
October 2010
utilise household level micro-data allowed the team to develop further the analysis on broader social
protection issues and scenarios, which have largely informed the main Strategy Paper.
The results of the three initial cost simulation exercises in this report must be read in the broader
context of the social protection scenarios discussed in the Strategy Paper. Moreover, a second round
of costing exercises could take place once more precise policy options have been identified by
stakeholders on the basis of the recommendations of the Strategy Paper. In this respect, one of the
purposes of this document is to provide UNICEF and the Government agencies involved in social
protection with some basic methodological indications for the development of a simple cost simulation
tool that could be used on an on-going basis.1
Following this introduction, this document begins (in Chapter 2) with an overview of the main results of
the cost simulations. The following chapters (Chapter 3, 4 and 5) discuss in greater depth each of the
three policy options considered in this document. Chapter 3 discusses the PSA expansion to children
in beneficiary households. Chapter 4 discusses the grant to children receiving nutrition support.
Chapter 5 discusses the grant to child-headed households. Each of these chapters discusses first the
transfer design assumptions underlying the simulation, together with other pre-feasibility
considerations, then presents the available empirical evidence and provides details on the results of
the costing exercise.
While the TORs mention a ‘cost-benefit analysis’, this document only encompasses a ‘cost analysis’.
Simulations of the potential benefits of the options analysed in this document are problematic due to
the lack of comprehensive data. However, some simulations of the poverty impact and cost
effectiveness of a range of policy options have been included in the main Strategy Paper.
1
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October 2010
Pre-feasibility and costing analysis of three short-term social protection reform options for
UNICEF Mozambique
2
Overview of results
The main objective of this document is to provide a preliminary approximation of the potential cost of
the following social protection options in Mozambique:
The full inclusion of children as secondary beneficiaries of the Programa de Subsídio de
Alimentos (PSA). This option is to broaden the coverage of the PSA to ensure that the
primary beneficiaries (elderly, chronically ill or disabled household heads) receive a
monthly transfer of MT 50 for all dependent children (younger than 18) living in the
household. While this is already prescribed in the manual, there are concerns that
children have been under-covered by the programme until now.
A new cash transfer scheme for children receiving nutrition support. This option would couple
existing nutrition supplementation programmes for malnourished children (younger than
5) with a temporary cash benefit to be provided during and after the nutrition intervention.
1. A new cash transfer scheme for child-headed households. The initial proposal was to
consider an option for a cash transfer to all children (younger than 18) living in childheaded households, i.e. households whose heads are younger than 18. After careful
consideration of the characteristics of this group it is recommended not to proceed with
this option, as we discuss further in the document.
Assumptions of costing scenarios
As a first step of the exercise, short-term cost projections were produced on the basis of the actual
coverage of existing programmes that are proposed to be reformed (PSA) or complemented (nutrition
support interventions). This approach is based on the fact that potential beneficiaries have already
been identified by implementation agencies, and presupposes the new/reformed grants can be easily
delivered to them, with roll-out happening relatively quickly (in one fiscal year). Under this static
scenario we produced estimates of the additional cost that the government and international partners
would have to bear to launch the interventions in the near future – say in 2011 – with no coverage
expansion of the underlying programmes (since the reference year).
As a second step, medium term cost projections were produced for a longer time horizon (2010-2014),
the same timeframe as that of the National Basic Social Security Strategy (ENSSB). For this exercise
key dynamic aspects were taken into account, including demographic and socioeconomics trends, as
well as future programme expansion within the reference timeframe. The medium term simulations are
based on the simple (and unrealistic) assumption that all eligible households are covered at any point
in time. This is very unlikely to happen in practice for both practical and more fundamental reasons.
On the one hand social protection reforms must be phased-in a gradual way, to allow for institutional,
operational and fiscal adjustment. On the other hand, one should expect a natural rate of coverage
inefficiency, for example due to targeting errors or beneficiaries self-selecting out of the scheme,
which would prevent the programme from reaching full take-up even when all financial and operational
resources were available.
The simulations based on the full set of eligible households provide an interesting reference point from
a comparative programme design perspective: the total amount of resources that would be ideally
spent if the programme achieved full coverage, full-take up and perfect targeting should be considered
by policy makers to assess alternative policy options. They constitute the other extreme of the simple
static scenario, as they are based on the assumption of filling the whole coverage gap of existing
programmes. Intermediate scenarios can be produced which allow for a smoother pattern of transition
from the current situation to the full coverage scenario, and may be extremely useful from an
implementation and financial planning standpoint. As it is extremely hard to forecast the patterns of
expansion of newly proposed programmes, we provide a phased plan only in the case of the PSA
expansion where more information is available (see Section 3.3.3).
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October 2010
Cost scenarios are related to national GDP and total government expenditure (TGE) (excluding debt
service) to analyse results in the perspective of the broader macroeconomic and public finance
context, and assess the fiscal space for alternative scenarios. Cost estimates are calculated at 2010
constant prices, and are compared with GDP and TGE in 2010, and, when applicable, with GDP and
TGE yearly projections for 2010-2014 produced by the IMF.2
Main Results
Table 2.1 summarizes the results obtained from the static simulation. The cost estimates are an
accurate projection for 2011 in so far as it is possible to assume that the coverage of existing
programmes will be similar to the reference year (2010 for the PSA and 2009 for nutrition support). It
is not possible to provide static cost estimates for the transfer to child-headed households as there in
no pre-existing programme in place. Full details on the calculations, the underlying design
assumptions, policy justification and available empirical evidence can be found for each proposed
policy option in the following chapters.
Table 2.1
Static cost simulation (2011)
Number of dependents/children already identified
Monthly transfer per dependent/child
Number of months per year
Total transfer cost (million MT)
Administrative costs (excl. set-up costs)
Total cost (million MT)
As a proportion of GDP (2010)
As a proportion of Total Gvt Exp (2010)
Grant for Children
PSA Extension to
(0-4) Receiving
All Dependents*
Nutrition Support
209,986
54,363
50
100
12
9
126
49
20.0%
30.0%
151
64
0.05%
0.02%
0.16%
0.07%
(ref. year 2010)
(ref. year 2009)
Notes: Based on existing programme coverage (primary PSA beneficiaries and children on nutrition
programmes). * Additional cost to the current PSA budget just for the inclusion of secondary beneficiaries.
Source: Author’s calculations. See more details in the next three chapters.
The simulation indicates that if all dependent household members in current PSA beneficiary
households were to receive the MT 50 benefit that is currently stipulated in the regulations, this would
require (in 2011) around MT 126 million in additional transfer costs. The total transfer cost of the PSA
would be around MT 492 million, roughly 73% more than the estimated total transfer value in 2009
(INAS, 2009)3. Note that the simulation refers generally to the category of additional dependents
(which includes all children younger than 18, spouses and other elderly household members) and not
exclusively children. This is due to data limitations that are discussed in detail in Chapter 3.3. The
fiscal space required to complete the coverage of secondary beneficiaries would amount to about
0.16% of total government expenditure in 2010.4
The cost of introducing a new transfer scheme for children receiving nutrition support (under the
design specifications described in Chapter 4) would be around MT 49 million, as we estimate that
there should be (in 2009) around 54,000 severely or moderately acute malnourished children younger
than 5 receiving nutritional supplementation from existing programmes implemented by MISAU or
WFP. The estimated cost, including 30% administrative but no start-up costs, would represent a rather
small portion of the total government budget in 2010 (0.07%). 2
2
Se Annex B for full detail of the macroeconomic and public finance figures used in the simulation.
3
MT 285 million, estimated from the number of direct and indirect beneficiaries reported.
4
Based on IMF projections. See Annex B.
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October 2010
Pre-feasibility and costing analysis of three short-term social protection reform options for
UNICEF Mozambique
We cannot provide a static cost simulation for the third policy option, as there is currently no specific
programme targeted to child-headed households which could be used as an underlying
implementation structure for beneficiary identification and roll out.
In Table 2.2 we resume the full-coverage cost projections for 2014 obtained for the three options
under consideration. Full details on the assumptions underlying the dynamic simulations and year by
year projections can be found in the next chapters.
Table 2.2
Full expansion and full coverage cost scenario (2014)
Number of eligible children
Monthly transfer per child
Number of months per year
Total transfer cost (million MT)^
Administrative costs (excl. set-up costs)
Total cost of the option (million MT)^
As a proportion of GDP (2010)
As a proportion of Total Gvt Exp (2010)
As a proportion of GDP (2014)
As a proportion of Total Gvt Exp (2014)
Full PSA Expansion Grant for Children
including all
(0-4) Receiving
Children (0-17)*
Nutrition Support
736,734
160,530
50
100
12
9
1,198
144
30.0%
35.0%
1,557
195
0.52%
0.07%
1.68%
0.21%
0.40%
0.05%
1.30%
0.16%
Grant for Children
(0-17) in Child
Headed
Households
39,237
100
12
47
40.0%
66
0.02%
0.07%
0.02%
0.06%
Notes: * Total programme cost, including main beneficiaries and all dependent children 0-17, but excluding other
categories of eligible dependents. Only in elderly-headed households, ^ At 2010 constant prices. Source: Author’s
calculations on MICS (2008) data, INE demographic projections and GDP and TGE projections produced by the
IMF (2009) and reported in Annex B.
As expected, the cost of expanding the PSA to all eligible children below 18 is much bigger than the
estimate from the static scenario. This is a direct consequence of the low coverage of the PSA (that
we estimate being around 34% of eligible elderly-headed households at present) and the fact that the
past programme expansion has been biased towards households with few children (see more details
in Chapter 3). Note that Table 2.2 provides a cost estimate of full PSA expansion to all eligible primary
beneficiaries and to all eligible secondary beneficiaries (children, spouses and other elder members)
in elderly-headed households5, including those already covered under the current budget. 6
Overall the simulation indicates that in 2014 INAS would require around MT 1,198 million (at 2010
constant prices) in transfer costs to cover 525,000 elderly household heads and roughly 735,000
children 0-17.7 This presupposes that all programme regulations concerning economic eligibility
criteria are maintained and strictly implemented as indicated in the current version of the manual of
operations, and the eligibility criteria for dependents are relaxed to include all children 0-17. The cost
projections are also based on the assumption that the value of the transfer and the income eligibility
threshold are adjusted for inflation throughout the timeframe of the simulation. The scale of the fiscal
implications of this full coverage scenario (0.5% of GDP in 2010, 0.4% of projected GDP for 2014)
calls for a careful assessment of the future expansion plans of the programme, especially as the
prospects for substantial real budget increases in the medium run are uncertain. We discuss the
implications in Chapter 3.
5
Cost estimates for the other categories of primary beneficiaries are provided in Chapter 3. We omit
them from this chapter as they rely on strong methodological assumptions due to data limitations.
6
Note that, instead, Table 2.1 before referred only to the additional cost of expanding coverage with
respect to the present cost.
7
This would correspond to MT 1,580 million to cover all 950,000 eligible secondary beneficiaries (see
chapter 3.1). In this table we restrict the simulation to dependent children, to compare the option with
other children based transfers.
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October 2010
The medium term cost forecast for the second policy option is based on the estimation of the total
number of severely and moderately acute malnourished children 0-59 months who would be eligible
for nutrition support in 2014. We estimate this on the basis of population projections and the
prevalence rate of acute malnourishment reported in the MICS (2008), obtaining a total number of
eligible beneficiary children of about 160,500. The simulation indicates that the total envelope of
resources required to run a scheme with the characteristics described in detail in Chapter 4 (100 MT
per child for 9 months) would be in the area of MT 144 million per year for transfer costs only. Even
assuming a generous share of administrative costs, the intervention seems to be feasible under the
fiscal space of MISAU or INAS, as it would represent roughly 0.16% of the total government budget (in
2014). The simulation presupposes that prevalence rates of acute malnutrition are stable with respect
to the population of reference over the period considered.
Finally, a new transfer scheme for children living in child-headed households would represent the least
expensive of the three policy options considered. This is a consequence of the fact that, on the basis
of available information in the MICS 2008, the target group would be very small. Estimates indicate
that there should be about 39,000 children 0-17 living in households where the head is younger than
18 (but we use a rather restrictive definition of child-headed households and these figures may
underestimate the phenomenon, as described in Chapter 5). This would result in a total transfer cost
of MT 47 million, to which would need to be added the start-up and administrative costs that would
arise from programme launch and implementation. On the basis of cost effectiveness and other more
substantive considerations on the social protection needs of this target group, in Chapter 5 we
conclude that it may not be advisable to proceed with the idea of launching such a scheme, at least in
the form of cash support. Strengthening existing in-kind support programmes, as the PASD, seems to
be a preferable option.
Administrative costs
The issue of administrative costs is particularly critical and sensitive in the context of the cost
simulations that are included in this report. It is hard to obtain reliable estimates of the current
administrative costs of existing programmes, or to project how these might change with scale-up (or
as a result of improved operational efficiency). It is also difficult to estimate the start-up costs for new
programmes. Moreover, it is not clear that start-up costs should be considered in a medium-term
costing exercise, as they should be absorbed by the programme over its whole course of operation. It
should be generally easier to obtain approximations of the administrative costs for on-going
programmes like the PSA. Unfortunately the evidence is fragmented. The standard working
assumption in MMAS/INAS budget documents (INAS, 2009) is that PSA administrative costs are 15%
of the value of the transfer. However, additional administrative costs may be reflected in: a) the undercoverage of households with dependents, and b) the incomplete provision of transfer over the course
of the year (as some beneficiaries may join the scheme after the start of the fiscal year and some
others leave the scheme and may not be replaced). Moreover the share of administrative costs used
in planning documents does not generally include the recurrent costs of INAS delegations and
headquarters. The comparison of the total PSA budget for 2009 (MT 403 million) and reported
numbers of main and secondary beneficiaries for the same year (respectively 167,000 and 141,000)
rather suggest a much higher share of administrative costs, in the area of 43%, which is probably still
a fraction of the real cost of operating the PSA, as it does not include the cost of INAS delegations and
HQ.8
In the context of this costing exercise, estimates are based on rough estimations of the possible
proportion of administrative costs, based on the nature of the policy intervention proposed and the
potential to create synergies with existing programmes and exploit economies of scales.
8
The cost of INAS Delegations and HQ are not part of the PSA budget allocation, and are not
reported under the INAS allocation in the State Budget, but separately as ‘transfers’ under the budget
line ‘encargos gerais do Estado’. It is not possible to know what proportion of any of these allocations
(INAS central, INAS provincial, or ‘PSA’) is actually for the PSA, as all these allocations are also partly
for the other INAS programmes.
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Pre-feasibility and costing analysis of three short-term social protection reform options for
UNICEF Mozambique
3
PSA full inclusion of children as secondary
beneficiaries
3.1
Design Considerations
The PSA is the main social transfer programme currently in place in Mozambique. It provides regular
monthly cash transfers to destitute elderly, disabled and chronically ill people living by themselves or
in households with little or no capacity to work. The transfer varies from MT 100 to MT 300 a month
(i.e. about USD 3-8), depending on the size of the household. The main (direct) beneficiary receives
MT 100 per month, plus an additional MT 50 for every dependent household member (secondary
beneficiary), up to a maximum of 4 secondary beneficiaries per household.
The programme targets destitute households without productive capacity, as the manual of operations
establishes that beneficiaries must live in households where the labour income per capita is less than
MT 100 per month.9 In 2009, the number of direct beneficiaries of the PSA reached 166,824, of whom
93.8% were elderly persons, 5.3% disabled and 0.9% chronically ill.
According to the current regulations, secondary beneficiaries can be a) the spouse(s) of the main
beneficiary, b) the parents and other elder relatives of the head and the spouse, and c) their direct
descendants (sons and daughters) if younger than 18 or disabled. In the case of grandsons,
granddaughters and other children younger than 18 who live in the household, the manual of
operations specifies that they can be considered as secondary beneficiaries only if they are orphans.
Although not explicitly clarified, the manual seems to restrict the eligibility to orphans of both parents.
In all cases the maximum number of 4 secondary beneficiaries applies.
In line with what is stated in the manual, there is a common understanding at the INAS national and
local levels that only direct descendants and orphans of both parents should be considered as
secondary child beneficiaries, at least in the case of elderly-headed households. The official protocol
followed by INAS in case of non-orphan children (or orphans of one parent), is to identify the parent(s)
and raise their awareness of the importance of their care-giver role as they “should be responsible for
the children”. Technical staff at local INAS delegations also reported that “abandoned children” can be
considered as eligible secondary beneficiaries in cases when at least one parent is alive but contact
with her/him cannot be established or responsibility is refused. It seems, however, that these
requirements may be very strictly applied in the field.
The current regulation leads to the exclusion of a large proportion of potentially vulnerable children
from the programme as it is very common that children are cared for by the grandparents, especially
when parents are absent, chronically sick or non-responsible. A broader inclusion of these children in
the PSA is surely justifiable on the grounds that they live in vulnerable households (as the head is
destitute and income is below a certain per capita threshold), and are therefore exposed to the same
risks as other household members. The fact that all dependent household members (spouses, other
elderly adults, direct descendants and orphans) apart from non-orphan children are eligible to be
considered as secondary beneficiaries cannot be justified on the grounds of equity. 10 It penalizes not
only children, but also the main beneficiaries, as the benefit per capita is smaller in bigger households.
UNICEF is therefore right in advocating for a broader inclusion of children in the programme.
9
The income threshold is calculated excluding informal and formal transfer and income from selfproduction. While it is not clear that this eligibility rule is implemented in the field by INAS delegations
at the time of the enrolment of beneficiaries, the simulation exercise is based on this definition of
household eligibility.
10
There are also issues with the definition of the household, as local delegations may consider that
children do not live in a beneficiary household if, for instance, they do not spend the whole week with
their grandparents. The way in which private transfers are considered in the calculation of income per
capita is not clear and not specified in the manual.
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October 2010
During discussions with INAS national staff concerns were expressed that relaxing the eligibility rules
on non-orphan children may incentivize artificial child fostering, encourage parents to abdicate their
responsibilities and result in fraud. However, the extent to which 50 MT would represent a sufficient
incentive for such behaviour is not clear. One possibility would be to combine the expansion to all
children with a revision of the cap on the total number of secondary beneficiaries, which could be
reduced to 3. However, this would also have the negative effect of penalizing (in relative terms) larger
(hence poorer) households.
Even within the current strict regulations there is a common understanding that dependent household
members, and particularly orphan children, are generally underserved by the programme. There are
three main reported reasons for such exclusion. First of all, the total coverage of the programme is still
relatively low. On the basis of IAF 2002/03 data and INE population projections, we estimate that the
number of destitute elderly currently receiving the PSA (156,311 in 2009) is only about 32% of the
total target group. A very substantial number of additional (orphan) children would be reached if all
eligible households were covered by the PSA. 11 Second, during the past programme expansion there
may have been a systematic bias towards the exclusion of bigger households, and particularly
households with (orphan) children. This means that there are proportionally more (orphan) children to
be covered in eligible households that are still to be served, than in currently served households.
Third, even within households that are currently receiving the PSA it seems that many (orphan)
children who should be eligible as secondary beneficiaries are not in fact registered in the programme,
despite the household head receiving the transfer. Not only may the PSA beneficiary selection be
biased in practice against eligible households with (orphan) children, but also many (orphan) children
in enrolled households may often not be being counted as secondary beneficiaries.
This situation may be a consequence of the incentives for INAS delegations, as local budgets are
allocated on the basis of the number of direct beneficiaries, regardless of the number of indirect
beneficiaries. Keeping the number of indirect beneficiaries low artificially creates additional financial
space to cover budget lines other than the mere transfer value, most notably administrative costs. In
the next section we assess whether these hypotheses can be confirmed on the basis of the available
evidence.
3.2
Evidence
Unfortunately the official administrative records do not provide details of the breakdown of secondary
beneficiaries by age or other categories. The consolidated national report for 2009 only indicates that
secondary beneficiaries were 140,643, on average 0.84 per direct beneficiary (INAS, 2009).
Complementary information on the distribution of secondary beneficiaries by type can be obtained
from the PSA impact evaluation dataset (Soares et al., 2009), but it must be borne in mind that the
dataset is only representative for a small sample of new beneficiaries in 2008-09, not PSA
beneficiaries as a whole (Table 3.1).
11
For a broader discussion on the pace and pattern of the future PSA expansion refer to the main
Strategy Paper.
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October 2010
Pre-feasibility and costing analysis of three short-term social protection reform options for
UNICEF Mozambique
Table 3.1
Distribution of PSA secondary beneficiaries in households
enrolled in 2008-09
proportion of PSA
beneficiary
households with
Secondary
Beneficiaries
Any type
Children 0-17
Spouse
Other elderly*
Other adults
average number of
secondary
beneficiaries per
PSA beneficiary
household
10.4%
2.7%
7.5%
0.1%
0.8%
0.122
0.033
0.075
0.001
0.012
proportion of second
beneficiaries who are
100.0%
27.5%
61.5%
1.1%
9.9%
Notes: * Other household members older than the head (main beneficiary) and the spouse. Source: Author’s
calculations on PSA impact evaluation dataset (2009).
According to the PSA evaluation data, the average number of indirect beneficiaries per direct
beneficiary is very low (0.12), and only 10.4% of beneficiary households has at least one secondary
beneficiary. Secondary beneficiaries are in most cases spouses (61.5%), followed by children 0-17
(27.5%). There are also roughly 10% of secondary beneficiaries that do not seem to belong to any of
the eligible categories, which we indicate in the table as other adults.
These figures are in stark contrast with the aggregated evidence available from administrative
sources, as official records (INAS, 2009) indicate that there are on average 0.84 secondary (indirect)
beneficiaries per beneficiary household (far above the 0.12 found by the PSA evaluation data).12 This
suggests that there has been a systematic bias towards lower coverage of secondary beneficiaries in
the recent PSA expansion (at least in 2008-09) compared with the proportion of secondary
beneficiaries in previously enrolled households. This might explain why the administrative data also
show a decline in the ratio of secondary to primary beneficiaries from 1.0 in 2008 to 0.84 in 2009.
Table 3.2 further disaggregates the PSA evaluation data on the distribution of PSA secondary
beneficiary children by age and orphan status. The first thing to note is that coverage is extremely low
throughout all groups. Most secondary beneficiary children are older than 13, and a very small fraction
of them are younger than 6. In terms of orphan status, in line with regulations, the biggest proportion
of secondary beneficiary children are orphans of both parents (36%), but there are also several
beneficiary children who are orphan of one parent (28%), while only 8% live with both their parents in
the household.
12
Part of the discrepancy is likely to result from the narrowly defined sampling framework of the PSA
impact evaluation
13
October 2010
Table 3.2
Distribution of PSA secondary beneficiary children (0-17) in
households enrolled in 2008-09
proportion of PSA
beneficiary
households with
Secondary
Beneficiaries
Children 0-17
0-5
6-12
13-17
Orphans of both parents
Orphans of one parents
Live without both parents
Live without one parents
Live with two parents
average number of
secondary
beneficiaries per
PSA beneficiary
household
2.7%
0.1%
1.2%
1.6%
0.9%
0.8%
0.5%
0.3%
0.1%
proportion of second
beneficiary children 017 who are
100.00%
4.0%
44.0%
52.0%
36.0%
28.0%
20.0%
8.0%
8.0%
0.033
0.001
0.015
0.017
0.012
0.009
0.007
0.003
0.003
Notes: Categories are mutually exclusive. Source: Author’s calculations on PSA impact evaluation dataset (2009).
Providing empirical evidence on the magnitude of the undercoverage of children, and particularly
orphan children, in the PSA is problematic because of data limitations. The official administrative
records do not contain disaggregated information about the number of secondary beneficiaries per
main recipient and household composition. Again, the only alternative source is the dataset collected
for the PSA impact evaluation (Soares et al., 2009), which, however, was designed to serve a different
purpose and is only representative of a very small and specific sub-group of PSA beneficiaries (in
2009).
Table 3.3 compares information obtained from the PSA evaluation dataset on the household
composition and average number of actual and eligible secondary beneficiariesin new PSA
beneficiary households. Despite current PSA regulations, all children 0-17 are considered as eligible
dependents.13
Table 3.3
Actual and eligible PSA secondary beneficiaries in households
enrolled in 2008-09
proportion of PSA
beneficiary
households with
Secondary
Beneficiaries
Any type
Children 0-17
Spouse
Other elderly*
Other adults
10.4%
2.7%
7.5%
0.1%
0.8%
average number
of secondary
beneficiaries per
PSA beneficiary
household
proportion of PSA
beneficiary
households with
Eligible
Dependents
0.122
0.033
0.075
0.001
0.012
average number
of eligible
dependents per
PSA beneficiary
household
56.7%
40.2%
32.9%
1.6%
-
proportion of
eligible PSA
dependents
covered in PSA
beneficiary
households
1.172
0.824
0.332
0.017
-
10.4%
4.1%
22.6%
7.7%
-
Notes: * Other household members older than the head (main beneficiary) and the spouse. Source: Author’s
calculations on PSA impact evaluation dataset (2009).
The number of secondary beneficiaries is remarkably low compared to the number of eligible
dependents in these new beneficiary households enrolled during the expansion in 2008-09. As
13
Eligibility is estimated on the basis of the assumption that at maximum 4 household members can
be secondary beneficiaries. All children below 18 and all household members who are older than the
head are considered to be eligible to become secondary beneficiaries, regardless of their relationship
with the head of the household. If there are more than 4 eligible secondary beneficiaries we assume
that they would be assigned to the programme in the following order: children 13/17, other elderly
members and spouses.
14
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UNICEF Mozambique
previously noted, the PSA impact evaluation suggests that only 10% of these new beneficiary
households has any dependents registered as secondary beneficiaries. However the same dataset
shows that in 56.7 % of the same beneficiary households there is at least one eligible dependent.
While there are on average only 0.033 (i.e. almost no) secondary beneficiary children below 18 per
beneficiary household, roughly 0.86 more children per household (0.895 – 0.036) should have been
registered.
Table 3.4 further disaggregates this analysis by type of children. It reveals that under-coverage of
children in recently enrolled beneficiary households is a serious issue, not only for non-orphan
children, but also for orphans of both parents, who are unambiguously eligible as secondary
beneficiaries according to the regulations in the PSA manual.14 On average only 18% of them are
registered as secondary beneficiaries, followed by orphans of one parent and other categories of
children, whose coverage is substantially lower. Although the PSA impact evaluation dataset provides
most probably a biased representation of the number of actual secondary beneficiaries, the
comparison shows that there is a significant proportion of (orphan) children to be covered in current
beneficiary households.
Table 3.4
Actual and eligible secondary beneficiaries in current PSA
beneficiary households
proportion of PSA
beneficiary
households with
Secondary
Beneficiaries
Children 0-17
Orphans of both parents
Orphans of one parents
Live without both parents
Live without one parents
Live with two parents
average number
of secondary
beneficiaries per
PSA beneficiary
household
2.7%
0.9%
0.8%
0.5%
0.3%
0.1%
proportion of PSA
beneficiary
households with
Eligible
Dependents
0.033
0.012
0.009
0.007
0.003
0.003
average number
of eligible
dependents per
PSA beneficiary
household
40.2%
4.4%
9.4%
16.8%
6.8%
10.0%
0.824
0.066
0.176
0.258
0.115
0.202
proportion of
eligible PSA
dependents
covered in PSA
beneficiary
households
4.1%
18.4%
5.3%
2.6%
2.3%
1.3%
Notes: Categories are mutually exclusive. Source: Author’s calculations on PSA impact evaluation dataset (2009).
A second remark comes from the comparison of the distribution and numbers of eligible dependents in
current new PSA households and theoretically eligible PSA households (Table 3.5). From MICS
(2008) data and IAF (2002/03) data we estimated PSA eligibility at the household level using either
the age criterion (households whose head is a male older than 59 or a woman older than 54) 15 or the
age criterion and the income criterion (households whose total labour income is below MT 100 per
capita per month)16. The information only refers to households with elderly heads (PSA definition), as
they represent the main category of PSA beneficiaries. The left hand side of the table indicates the
scale of coverage and distribution of secondary beneficiaries that one would expect to find if all eligible
elderly received the PSA and all eligible dependents were registered. In such a full coverage scenario
there would be on average between 1.8 and 2.1 secondary beneficiaries per household, most of
whom (1.4 to 1.7 per households) would be children below 18. This is much higher than the actual
ratio of 0.84 secondary beneficiaries per primary beneficiary shown by the national administrative
data. The contrast is even sharper with the ratios shown by the PSA evaluation for households newly
enrolled in the PSA in 2008/09.
14
If there are more than 4 eligible secondary beneficiary children we assume that they would be
assigned to the programme in the following order: orphans of two parents, orphans of one parent,
abandoned children and other children.
15
We follow the definition of household head that is used in the MICS and IAF.
16
Adjusted for inflation between 2002/03 and 2010.
15
October 2010
Table 3.5
Actual new PSA dependents and eligible PSA dependents (elderlyheaded households only)
proportion of PSA
beneficiary
households with
Eligible
Dependents
average number
of eligible
dependents per
PSA beneficiary
household
proportion of PSA
eligible
households with
Eligible
Dependents
average number
of eligible
dependents per
PSA eligible
household
Age criterion only
Any type
Children 0-17
Spouse
Other elderly*
55.9%
38.7%
32.5%
1.3%
1.119
0.779
0.326
0.013
PSA Eval. (2009)
75.8%
61.3%
37.1%
1.6%
proportion of PSA
eligible
households with
Eligible
Dependents
average number
of eligible
dependents per
PSA eligible
household
Age and income criteria
1.817
1.426
0.374
0.016
MICS (2008)
81.0%
66.2%
40.5%
2.1%
2.080
1.650
0.407
0.023
IOF (2003)
Notes: Elderly-headed households only; * Other household members older than the head (main beneficiary) and
the spouse. Source: Author’s calculations on PSA impact evaluation dataset (2009), MICS (2008) and IAF
(2002/03).
The PSA evaluation data show that, in these new PSA beneficiary households, the average number of
eligible dependents is much lower (1.1 in total, of which 0.8 are children below 18) than in all
theoretically eligible households. This provides empirical confirmation of the fact that PSA households
recently enrolled in the programme have been selected in a biased way, excluding households with
dependents and particularly with children.17
Table 3.6 shows that during the expansion priority has been given to small households. While there is
a substantial number of elderly-headed households containing other adult household members who
would be eligible for the PSA on the basis of the socio-economic targeting criteria, most efforts have
focussed on isolated elderly people.
Table 3.6
Characteristic of actual new PSA households and eligible PSA
households (elderly-headed households only)
PSA beneficiary
households
Household size
Average number of adult members (18+, non elderly)
2.49
0.80
PSA eligible household
Age and income
Age criterion only
criteria
3.80
1.11
4.15
0.93
Avg, number of non-PSA beneficiary adult members
(18+, non elderly, non spouse, non ascendents)
0.01
PSA Eval. (2009)
0.67
MICS (2008)
0.64
IOF (2003)
Notes: Elderly-headed households only. Source: Author’s calculations on PSA impact evaluation dataset (2009),
MICS (2008) and IOF/IAF (2003).
In summary, although there are severe data limitations, the available evidence supports the
hypothesis that there is a significant under-coverage of (orphan) children in the PSA and provides
17
A final secondary remark has to do with the differences in household composition between eligible
households when the income criterion is imposed in addition to the age criterion. If the income
criterion used for targeting were strongly correlated with traditional poverty measures, one would
expect the household composition to change substantially when the sample is restricted to
households below the income threshold. In fact, poorer households are generally bigger. In Table 3.5
we don’t find any such major difference. This confirms the hypothesis (fully developed in the Strategy
Paper) that the current targeting rule of the PSA is not efficient. We also find that most of the observed
differences are in fact due to the change in the data source (MICS vs. IAF) and reference time (2008
vs. 2002/03). Using IAF 2002/03 data only, we find that households below and above the income
threshold have an almost identical household composition.
16
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some insights to estimating its magnitude. A more disaggregated administrative data system is
required to monitor progress appropriately.
3.3
Cost simulation
3.3.1
Static scenario
As a very first step, the costing exercise for the PSA expansion to children should provide an estimate
of the additional resources that would be required to ensure full coverage of eligible dependents in
households that are already receiving the transfer.
The simulation of this simple scenario is complicated by a number of factors:
The conflicting evidence on the current number of dependent beneficiaries per direct
beneficiary between administrative records and PSA evaluation data. Here we take the
view that official records should be trusted and used as a basis of the simulation, due to
the low representativeness of the PSA evaluation dataset.18
The lack of information on the distribution of indirect beneficiaries by age and relationship
with the household head in the official administrative sources. This implies that the cost
simulation of the PSA expansion of dependents cannot be children (or orphan) specific
but has to refer to the category of secondary beneficiaries as a whole.
The lack of information on the composition of beneficiary households in official administrative
data sources. This makes it impossible to directly estimate the coverage gap in
beneficiary households and forces us to use eligible households (whose characteristics
can be simulated using existing survey data) as a benchmark.19
 The impossibility to replicate the categorical eligibility rules that apply to PSA households
with chronic ill members or persons with disabilities in other survey data. This means that
the cost can only be simulated for elderly beneficiaries, and the total cost can be
projected proportionally, on the basis of the distribution of beneficiaries per category in
2009.20
Based on the above, the simulation rests on the following assumptions:
18
We also undertake a costing simulation only based on PSA evaluation data, obtaining very similar
results to those obtained using administrative and survey data. Under the same assumptions
described below, the total estimated additional transfer cost for full coverage of eligible dependents in
2010 would be MT 135 millions.
19
This further complicates the exercise as eligible households appear to contain a higher number of
eligible dependents than actual beneficiary households. With this caveat in mind, one can use eligible
households to estimate the coverage gap, considering that this will produce an upper bound of the
estimated expenditure.
20
We assume that, as in 2009, disabled and chronically ill beneficiaries will be respectively 5.3% and
0.9% of the total of beneficiary households. We also assume that the average number of additional
dependent to be included as secondary beneficiaries is common across the three categories. This
assumption is highly questionable, as the PSA evaluation data shows that the number of current and
eligible secondary beneficiaries in newly enrolled households is bigger in households with chronic ill
and disable main beneficiary than in elderly headed households. However: a) there is no
disaggregated administrative data to estimate the coverage gap by category, b) the low coverage of
non-elderly headed households implies that total cost estimates are not very sensitive anyway to
changes in assumptions for these groups.
17
October 2010
The value of the transfer to dependent beneficiaries is maintained at the level of MT 50 per
household member, with a maximum of 4 beneficiaries per household;
 The eligibility rules are relaxed in such a way to include as secondary beneficiaries all
children younger than 18, and all household members older than the household head
and the spouse, regardless of their relationship with the household head, as well as all
spouses of the household head.
The calculation is based on the following simple steps:
The average number of dependents per beneficiary household was 0.84 in 2009 (INAS,
2009).
The average number of dependents per household with an elderly member should be 1.81
(MICS, 2008).21
There are on average 0.977 households members to be included as additional dependents
in every current PSA beneficiary household (elderly only).
 This means an extra transfer value per current beneficiary household of MT 586 per year.
At the level of coverage (for the three categories) expected for 2010 (215,000) the simulation
provides an estimate of an additional MT 126 million per year. This includes the additional
transfer value but excludes extra administrative costs that may arise as the number of
indirect beneficiaries per direct beneficiary increases. Under this scenario the number of
indirect beneficiaries should rise by around 210,000, a large proportion of whom (at least
70%) would be children below 18.
3.3.2
Full coverage projections 2010-2014
In order to produce reliable medium-term projections of the cost of the PSA expansion to children two
main elements were considered initially:
Projections take into account population dynamics by age group. This is particularly
important for the PSA, as the elderly and the children are prone to substantial
demographic changes in a relatively short period of time.
 Projections take into account inflation. The main implication is the effect of inflation on the
effectiveness of the income eligibility threshold and on the value of the transfer in the
medium run. The working assumption for this simulation was that the current levels of the
income threshold (MT 100 per capita) and transfer value (MT 100 and 50) would be
revised on a yearly basis according to inflation. This is the same as assuming that the
income threshold and benefit level would be maintained constant in real terms.
More substantially, producing medium term projections for the coverage of children under the PSA fits
into a broader discussion on the future expansion of the PSA. Provisional plans for the PSA expansion
have been envisaged in the ENSSB, including a medium term costing scenario, and this is discussed
in the main Strategy Paper. From a design perspective, discussions on the PSA expansion should
reconsider three key issues: the categories of beneficiaries entitled to receive the benefit, the poverty
targeting, and the value of the transfer. In the main Strategy Paper we argue that a readjustment of
the current design on these three fronts is advisable. As the discussion of these issues is still
preliminary, at this stage the simulations were based on the current PSA (official) design in terms of
21
We use MICS as reference, and not IAF for two reasons. First, MICS data is more up to date.
Second, using IAF 2002/03 data only, we find that households below and above the income threshold
have an almost identical household composition. Third, the extent to which the income eligibility
criterion is actually enforced in the field is not clear. A similar approach based on the IAF data
provides a transfer cost estimate of MT 166 million.
18
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UNICEF Mozambique
categorical and economic eligibility criteria, the main new assumption being, as mentioned, a broader
definition of eligibility of secondary beneficiary compared to what in the PSA manual - to include all
children 0-17 - and inflation adjustment for the purpose of simplifying calculations.
A critical implication is that we base the simulation on the assumption that the manual regulations
regarding economic targeting are followed strictly in the field. The assumption of the simulation is that,
as per operational manual, the economic eligibility is established on the basis of wage income only. In
the field, INAS permanentes appear not to be strictly applying this criterion, as they do try to take into
account other resources such as assistance from extended family members. They also attempt to
assess the quality of housing, ownership of livestock and other assets and, to some extent, levels of
consumption. However, the criteria for such an assessment are not spelled out in the Procedures
Manual and in practice it seems that much is left to the interpretation of individual permanentes and
INAS staff. We argue in the Strategy Paper that the current economic targeting should be improved,
and the regulations should be revised.
As mentioned, the simulation scenario represents an upper bound of the expenditure that can be
faced at the end of the period (2014) in a full coverage setting. In this case by full coverage we mean
registration of all eligible main beneficiaries and secondary beneficiaries. The concept of secondary
beneficiary is also approached in a broad sense, including spouse, all household members who are
older than the head and the spouse, and all children below 18, regardless of whether or not they are
orphans.
The full coverage simulation was undertaken in the following steps: 1) estimating the number of
eligible households in the medium run; 2) estimating the number of eligible dependents in the medium
run, and; 3) calculating the projected cost.
1) Estimating the number of eligible households in the medium run
Demographic trends were considered in the context of the simulation based on the population
projections that are provided by INE on an age-by-age basis22 (based on the 1997 census). The most
appropriate approach can only be followed for the case of elderly-headed households: from existing
survey data we estimate the probability that an elderly person in the relevant age cohort is head of the
household (MICS, 2008), and the probability that an elderly-headed household has an income per
capita below MT 100 (IAF, 2002/03). These probabilities are then applied as a constant to
demographic projections of the relevant age groups, in order to obtain a number of eligible elderlyheaded households at every point in time.
The results of the projection are summarized in Table 3.7, whereas additional details on the steps and
the key underlying assumptions are provided in Annex A. The estimation suggests that in 2010 there
should be around 505,000 elderly-headed households eligible for the PSA in Mozambique based on
current official regulations. Between 2010 and 2014 the number is expected to rise to 564,000, making
an 11.6% increase.23 The simulation also shows that only about 35% of all elderly-headed households
are not eligible to the PSA in the period considered, as they fail the official economic eligibility test
(their income per capita is higher than 100 MT per month).
22
These were calculated in 2004 based on 1997 census data and using an urban-rural model. They
seem to be rather accurate, as they correctly forecast the total population registered in the 2007
census.
23
The estimated number of elderly household heads is substantially lower than the equivalent figure
included in the ENSSB simulation (923,244 in 2014), as the latter does not take into account the
economic eligibility criterion and follows a different approach for the calculation of eligible households
according to the age criterion.
19
October 2010
Table 3.7
Number of PSA eligible elderly households: 2010-2014 projections
Elderly individuals
Elderly household heads
Eligible elderly household heads
2010
1,163,657
751,398
504,947
2011
1,195,005
770,857
518,117
2012
1,228,562
791,740
532,244
2013
1,264,550
814,163
547,409
2014
1,303,154
838,257
563,700
2010-2014
Change
12.0%
11.6%
11.6%
Source: Author’s calculations on INE demographic projections and MICS (2008) data.
The simulation of the number of eligible households for other categories of beneficiaries, notably
chronic ill members and disabled, is complicated by the fact that the categorical eligibility rules that
apply to these PSA target groups cannot be replicated with available survey data. In the absence of
updated census data, a simple (and rough) alternative is to assume that the final number of chronic ill
and disabled eligible households is a fixed proportion of the number of eligible elderly-headed
households. An estimate of this proportion can be obtained from the administrative records of
currently covered households (INAS, 2009) 24, though they are likely to be affected by the current
under-coverage of disabled and chronic sick. The results are shown in Table 3.8.
Table 3.8
Number of PSA eligible households: 2010-2014 projections
2010
PSA Eligible households
Elderly
Disabled
Chronic Ill
Total
504,947
29,003
4,959
538,908
2011
518,117
29,759
5,088
552,964
2012
532,244
30,570
5,227
568,041
2013
547,409
31,441
5,376
584,226
2014
563,700
32,377
5,536
601,613
Source: Author’s calculations on INE demographic projections, PSA impact evaluation (2009) and MICS (2008)
data
2) Estimating the number of dependents in the medium term
The second step in the simulation involved estimating the number of eligible dependents. Again, this
entails factoring the demographic component into the simulation, as well as considering household
composition and eligibility issues. The number of eligible dependents must also be adjusted for the
fact that the maximum number of dependent beneficiaries is 4. While for spouses and other elderly
members one can assume that the average number per eligible households is constant over time,
projecting the number of children dependents in accordance with demographic trends is crucial, as we
expect that numbers may be heavily affected by demographic trends over the 2010-2014 timeframe.
The details of the approach that was followed are described in Annex A, while the results (for elderlyheaded households) are reported in Table 3.9.
The number of eligible dependent children 0-17 in elderly-headed households should rise from
728,000 to 788,000 between 2010 and 2014. The simulation suggests that, as the number of eligible
elderly-headed households grows faster than the total number of children in this type of households,
the average number per household will fall by 3.1% over the period considered.
24
It rest on the assumption that eligible disabled and chronically ill beneficiaries are respectively 5.3%
and 0.9% of the total of eligible households, i.e. there is a constant linear relationship between the
distribution of beneficiary households and eligible households. This is indeed a very questionable
assumption, as coverage has possibly been biased towards elderly headed households in the past. It
is however the only available option, as other survey based estimates depend substantially on the
definitions used (e.g. the 2007 population census indicates that 2.3% of the population has some form
of disability, while the IAF 2002/03, presumably using a different definition, put the proportion of
disabled adults at 7%), which are not compatible to those used by the PSA.
20
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Table 3.9
Number of PSA eligible dependent children in elderly-headed
households. 2010-2014 projections.
2010
Children 0-17
Children 0-17 in elderly headed households
Average number of children 0-17 in ederly headed
household
Average number of children 0-17 below the threshold
of 4 in ederly headed household
Eligible dependent children 0-17 in elderly headed
households
2011
10,791,736
1,227,831
2012
11,000,338
1,251,565
2013
11,215,044
1,275,993
2010-2014
Change
2014
11,436,893
1,301,234
11,668,444
1,327,578
8.1%
8.1%
1.63
1.62
1.61
1.60
1.58
-3.1%
1.44
1.43
1.42
1.41
1.40
-3.1%
728,413
742,627
757,251
772,360
788,124
8.2%
Source: Author’s calculations on INE demographic projections and MICS (2008) data
Again, it is not possible to follow the same steps for the other two categories of eligible households.
Alternatively, the average number of dependents per household can be adjusted proportionally to the
ratio of secondary beneficiaries reported by INAS in 2009. Total projections are summarized in Table
3.10.
Table 3.10
Number of PSA eligible dependent children. 2010-2014
projections.
2010
PSA eligible dependents
Elderly
Disabled
Chronic Ill
Total
925,828
96,156
20,944
1,042,928
2011
945,191
98,664
21,491
1,065,345
2012
965,338
101,354
22,076
1,088,768
2013
986,376
104,242
22,706
1,113,323
2014
1,008,509
107,344
23,381
1,139,234
Source: Author’s calculations on INE demographic projections, PSA impact evaluation (2009) and MICS (2008)
data
3) Calculating the projected cost
Having put together the different components of the simulation, Table 3.11 and Table 3.12 describe
the main results of the full-coverage costing exercise for the PSA, respectively for elderly-headed
households and all PSA beneficiary types. Financial figures can be interpreted as real nominal
amounts at 2010 values, as no adjustment for inflation has been done (and under the assumption that
the value of the transfer is adjusted for inflation over the period of reference).
Table 3.11
PSA expansion (elderly-headed households only) 2010-2014 cost
projections
Eligible elderly headed households
Eligible dependents
of which children 0-17
Total transfer cost (Million MT)^
As a proportion of GDP (in 2010)
As a proportion of Tot Gvt Exp (in 2010)
As a proportion of GDP
As a proportion of Tot Gvt Exp
2010
471,922
865,277
680,773
1,085
0.36%
1.17%
0.36%
1.17%
2011
484,258
883,422
694,096
1,111
0.37%
1.20%
0.35%
1.16%
2012
497,488
902,301
707,802
1,138
0.38%
1.23%
0.34%
1.11%
2013
511,690
922,013
721,962
1,167
0.39%
1.26%
0.32%
1.05%
2014
526,944
942,749
736,734
1,198
0.40%
1.30%
0.31%
1.00%
Notes: ^ At 2010 constant prices. Source: Author’s calculations on MICS (2008) data, INE demographic
projections and GDP and TGE projections produced by the IMF (2009) and reported in Annex B.
Table 3.12 indicates that, under current regulations, in 2014 the PSA scheme would cost MT 1,314
million, if all eligible households (primary beneficiaries) and all eligible dependent, including all
children 0-17 were registered. The cost estimates are not additional to the current PSA cost, as they
include households that are already served and dependents that are already considered as secondary
21
October 2010
beneficiaries under the current programme. Just because of demographic trends, the cost of running
the PSA at full scale rises in real terms by 10.5% in the period considered for the simulation.
According to the available macroeconomic projections, the amount or resources required is estimated
to decrease in relative terms to GDP and total government expenditure, as the economy is expected
to grow at a sustained pace between 2010 and 2014.
Table 3.12
Cost projections for PSA expansion (all PSA beneficiary types),
2010-2014
Eligible households
Eligible dependents
Total transfer cost (Million MT)^
As a proportion of GDP (in 2010)
As a proportion of Tot Gvt Exp (in 2010)
As a proportion of GDP
As a proportion of Tot Gvt Exp
2010
503,662
974,719
1,189
0.40%
1.29%
0.40%
1.29%
2011
516,828
995,725
1,218
0.41%
1.32%
0.38%
1.27%
2012
530,948
1,017,671
1,248
0.42%
1.35%
0.37%
1.22%
2013
546,104
1,040,677
1,280
0.43%
1.38%
0.35%
1.15%
2014
562,384
1,064,950
1,314
0.44%
1.42%
0.34%
1.10%
Notes: ^ At 2010 constant prices. Source: Author’s calculations on MICS (2008) data, INE demographic
projections and GDP and TGE projections produced by the IMF (2009) and reported in Annex B.
A valid comparison point for this costing analysis is represented by the amount of resources that
would be required if the current narrow focus on orphans (of both parents) were maintained. As Table
3.13 shows, the number of secondary beneficiary children would be extremely small (just 20,300 in
2014), but the fall in costs would be less than proportional to the fall in the number of children, as a
large fraction of resources would be still devoted to primary beneficiaries.
Table 3.13
Cost projections for PSA expansion (elderly-headed households
with inclusions of double orphans only), 2010-2014
Eligible elderly headed households
Eligible dependents
of which orphans of both parents (0-17)
Total transfer cost (Million MT)^
As a proportion of GDP (in 2010)
As a proportion of Tot Gvt Exp (in 2010)
As a proportion of GDP
As a proportion of Tot Gvt Exp
2010
471,922
203,291
18,788
688
0.23%
0.74%
0.23%
0.74%
2011
484,258
208,482
19,155
706
0.24%
0.76%
0.22%
0.74%
2012
497,488
214,032
19,534
725
0.24%
0.78%
0.21%
0.71%
2013
511,690
219,975
19,924
746
0.25%
0.81%
0.21%
0.67%
2014
526,944
226,347
20,332
768
0.26%
0.83%
0.20%
0.64%
Notes: ^ At 2010 constant prices. Source: Author’s calculations on MICS (2008) data, INE demographic
projections and GDP and TGE projections produced by the IMF (2009) and reported in Annex B.
3.3.3
Alternative expansion plans 2010-2014
From an implementation standpoint, the PSA expansion would have to be phased in progressively,
but the pace of coverage expansion that can be afforded has not been defined. Also, it is difficult to
provide forecasts of the efficiency of the roll-out and programme take-up. In this context, the
simulation provides costs estimates in a full-coverage and full take up scenario. It will obviously take a
significant time for the project to reach full geographical coverage, and take-up will also increase
steadily, probably never reaching 100% after inclusion and exclusion errors are accounted for.
This section thus presents alternative roll-out scenarios to achieve full coverage of an expanded
version of the PSA in 2014. In Table 3.14 we estimate that reaching complete expansion in 2014,
starting from actual coverage rates in 2009, would require a real budget increase of 36% every year.
In order to maintain a constant increase in real budget, in the first years most of the expansion should
be focussed mainly on enrolling eligible dependents of current beneficiary households.
22
October 2010
Pre-feasibility and costing analysis of three short-term social protection reform options for
UNICEF Mozambique
Table 3.14
Roll out of the PSA expansion – Scenario 1
Primary Beneficiaries
% increase
Secondary Beneficiaries (spouses, ascendents
and all children 0-17)
% increase
Total transfer cost (Million MT)^
As a proportion of GDP (in 2010)
As a proportion of Tot Gvt Exp (in 2010)
As a proportion of GDP
As a proportion of Tot Gvt Exp
Projected real budget increase
2009
166,824
2010
170,995
2%
2011
224,609
31%
2012
304,997
36%
2013
414,157
36%
2014
562,384
36%
140,643
302,052
115%
386
0.15%
0.50%
0.13%
0.42%
36%
425,327
41%
525
0.20%
0.68%
0.16%
0.55%
36%
577,553
36%
713
0.27%
0.92%
0.21%
0.69%
36%
784,261
36%
968
0.37%
1.25%
0.27%
0.87%
36%
1,064,950
36%
1,314
0.50%
1.69%
0.34%
1.10%
36%
285
Notes: ^ At 2010 constant prices. Source: Author’s calculations on MICS (2008) data, INE demographic
projections and GDP and TGE projections produced by the IMF (2009) and reported in Annex B.
Sustaining such a high pace of expansion may in fact not be feasible from a fiscal point of view.
Coverage expansion has not been so aggressive in 2010, and projections for 2011 are that the budget
will increase by less than 4% in real terms. In Table 3.15 and Table 3.16 we provide forecasts for less
ambitious expansion plans, assuming that respectively 15% and 3% real budget increase for the PSA
can be achieved. In both cases, only a modest expansion of the number of primary beneficiaries can
be achieved, while most resources are devoted to including dependents of current primary
beneficiaries.
Table 3.15
Roll out of the PSA expansion – Scenario 2.
Primary Beneficiaries
% increase
Secondary Beneficiaries (spouses, ascendents
and all children 0-17)
% increase
Total transfer cost (Million MT)^
As a proportion of GDP (in 2010)
As a proportion of Tot Gvt Exp (in 2010)
As a proportion of GDP
As a proportion of Tot Gvt Exp
Projected real budget increase
2009
166,824
2010
170,995
2%
2011
175,269
2%
2012
185,261
6%
2013
213,050
15%
2014
245,007
15%
140,643
203,445
45%
327
0.13%
0.42%
0.11%
0.35%
15%
276,711
36%
376
0.14%
0.49%
0.12%
0.39%
15%
350,816
27%
433
0.17%
0.56%
0.13%
0.42%
15%
403,438
15%
498
0.19%
0.64%
0.14%
0.45%
15%
463,954
15%
572
0.22%
0.74%
0.15%
0.48%
15%
285
Notes: ^ At 2010 constant prices. Source: Author’s calculations on MICS (2008) data, INE demographic
projections and GDP and TGE projections produced by the IMF (2009) and reported in Annex B.
Table 3.16
Roll out of the PSA expansion – Scenario 3.
Primary Beneficiaries
% increase
Secondary Beneficiaries (spouses, ascendents
and all children 0-17)
% increase
Total transfer cost (Million MT)^
As a proportion of GDP (in 2009)
As a proportion of Tot Gvt Exp (in 2009)
As a proportion of GDP
As a proportion of Tot Gvt Exp
Projected real budget increase
2009
166,824
2010
166,824
0%
2011
166,824
0%
2012
166,824
0%
2013
166,824
0%
2014
166,824
0%
140,643
156,295
11%
294
0.11%
0.38%
0.10%
0.32%
3%
172,463
10%
304
0.12%
0.39%
0.10%
0.32%
3%
189,164
10%
314
0.12%
0.40%
0.09%
0.31%
3%
206,417
9%
324
0.12%
0.42%
0.09%
0.29%
3%
224,239
9%
335
0.13%
0.43%
0.09%
0.28%
3%
285
0.11%
0.37%
Notes: ^ Real values (2010). Source: Author’s calculations on INE demographic projections, PSA impact
evaluation (2009) and MICS (2008) data
23
October 2010
4
Grant for children receiving nutrition support
4.1
Design considerations
Providing some sort of social protection support to households with malnourished children enrolled in
existing nutrition support programmes was identified by stakeholders as a feasible short-term option to
expand existing social protection mechanisms in Mozambique. The idea rests on the consideration
that a broader safety net (what is generally referred to as a “wrap-around”) is required for households
incurring transitory or chronic malnutrition, besides the specific “clinical” treatment that is provided via
food supplements.25 Such a scheme would serve the purpose of ensuring that the nutritional treatment
is more effective and sustainable in itself, as well as avoiding dependency in the longer run.
From the perspective of design of social protection instruments, the benefit of the proposed scheme
rests on the fact that food insecurity and chronic malnutrition would be used as an “indirect” targeting
mechanism to identify vulnerable households: the ultra-poor in cases of chronic malnutrition, and
households facing extremely severe temporary shocks in the cases of transitory malnutrition. From an
implementation standpoint, mounting a cash transfer intervention linked to pre-existing nutrition
support programmes could also create synergies and economies of scales in the identification of
beneficiaries and the whole programme operation, provided that adequate institutional arrangements
are established among the responsible government bodies.
Yet there are two major obstacles to the introduction of such a scheme that need to be considered
carefully. First, most households receiving nutrition support cannot easily be seen as part of the
category of labour-constrained households without productive capacity that has traditionally been
used to define the scope of social cash transfers in Mozambique, outside an emergency context.
Second, concerns may arise in relation to the risk of duplicating efforts when concentrating different
interventions (in this case nutrition support and cash transfers) on the same target populations. The
current approach is rather to reach different segments of the population with different programmes, in
an attempt to increase the number of beneficiaries of the social protection “system” horizontally, rather
than increase its effectiveness vertically.
Both concerns can be addressed by lining up the objectives of the new scheme to the accumulation of
human capital of the new generations in ultra-poor/ultra-vulnerable households. The characteristics of
living conditions of the ultra-poor/ultra vulnerable and the urgency of early human capital investment
make situations like the one of malnourished children unique, and call for a special intervention
response. The new transfer can be justified on the basis of: a) investing more resources where the
risk is bigger (human capital is already severely depleted); and b) investing more resources where the
impact can be higher (gains from human capital investments are bigger) and consequences long
lasting, particularly in an inter-generational perspective.
As for the type of vulnerability that one would aim at mitigating when targeting this group of children,
unfortunately it is not possible to assess the extent to which food insecurity is a transitory or a chronic
state on the basis of informed evidence. Still, one can assume that it depends on a combination of
underlying traits of the household (socioeconomic composition, knowledge, livelihood strategies) as
well as idiosyncratic shocks related to agricultural productivity, weather cycle and natural disasters.
Only a part of these factors can be related to long term chronic vulnerability.
In this respect, the design of the transfer can follow two approaches. If the categorical targeting is
combined with some sort of socioeconomic targeting to select only chronically food insecure
households, the scheme can be conceived as a form of regular long term support to the household,
with the aim of building up assets and achieving self sufficiency. This however clashes with the narrow
focus of existing social transfers (like the PSA) on labour-constrained households and the
feasibility/complexities of such a targeting approach. Alternatively, the scheme can be conceived as a
25
A similar argument is given in support of broad social protection schemes targeted to households of
AIDS patients receiving ARTs, as opposed to clinically grounded “food for prescription”.
24
October 2010
Pre-feasibility and costing analysis of three short-term social protection reform options for
UNICEF Mozambique
quasi-emergency intervention, where cash is to supplement and reinforce nutritional therapy, and to
tackle temporary shocks in the household. It seems that this second option is more viable in the
current institutional setting.
Two other design considerations follow from the quasi-emergency orientation.
As the duration of the nutrition support programmes is generally rather short (3 months) the
initial proposal is based on the assumption that the benefit will be provided during this
period and 6 months after the nutrition support intervention. Providing an extra cash
injection while the nutrition therapy is taking place would have the benefit of avoiding the
dilution of food supplements amongst other household members. Providing support after
the nutrition support ends would serve the purpose of preventing the immediate reversal
of the nutritional progress that has been achieved through food supplements and
improving food security in the short/medium run. Depending on the age of the child, there
may be reasons to extend the measure of “protection” of human capital investment
beyond the 9 months, to cover critical steps/phases of early cognitive development,
without distorting the temporary nature of the scheme.
 The transfer can be paid on a per-child basis or take also into account the household
structure (for example by considering secondary dependents). While adjusting the
transfer value to the household size and composition would put the emphasis on
household level vulnerability and food security, paying a fixed amount per-child may be
operationally easier and may strengthen the link between the cash benefit and the
protection of the nutritional status of the malnourished child. However the effect of the
transfer would also be diluted in larger households if the transfer resources are spent in
general household consumption, or for other children. The current working proposal is to
pay a fixed amount per-child, and the reference value used for the simulation is MT 100
per month.26 A possible alternative would be to also consider other children 0-4 (not
receiving nutrition support) as beneficiaries, hence maintaining the focus of the scheme
on food security of vulnerable children. The possibility of paying per child also paves the
way for further consideration of the case for inclusion of conditionality mechanisms to
increase sustainability and co-responsibility. One proposal (implicit condition) is to link
payments to regular nutrition screening of the child (during and after nutrition support at
the “consulta de criança en risco”), but a broader feasibility study is required to examine
whether there are appropriate institutional conditions. 27
The initial proposal is to focus the intervention on children aged 0-59 months who are severely acute
malnourished or moderately acute malnourished and receive nutrition support supplementation from
the Ministry of Health (MISAU) or WFP (see more detail below on such programmes). Due to the
current plan of providing a food basket to households with HIV/AIDS patients, children with HIV/AIDS
were excluded from the initial simulation. In discussions with stakeholders the importance of including
moderate and severe acute malnourished pregnant women receiving nutrition support was also
stressed. Malnourished pregnant women used to be a target group of the PSA in its early stage of
operation, but coverage was never significant and they were later dismissed as a consequence of a
mismatch with the long term social assistance nature of the programme and the complexities for INAS
to identify malnourished women in the field. No explicit collaboration had been established between
INAS and MISAU at that stage.
26
In line with the amount paid by the PSA to the principal beneficiary. There is no concept of
secondary beneficiary, but in the simulation it is assumed that the transfer would cover up to a
maximum of 4 children receiving (or having received) nutrition support per household
27
Other proposals included conditioning payments on participation in food security training or regular
vaccinations, though the latter may not be viable within the 9 months framework.
25
October 2010
Now the proposed scheme could: a) fall under the umbrella of a reformed Programa de Apoio Social
Direito (PASD), which is INAS’s main instrument for in-kind social emergency responses; or b) be run
as a separate programme by MISAU as part of the ‘social action’ in the health sector contemplated in
the ENSSB. In both cases there are significant opportunities to share knowledge and eventually
exploit synergies between MISAU and INAS, especially with respect to the processes of beneficiary
identification, transfer payment and supervision.
4.2
Evidence on existing nutrition support interventions
MISAU is coordinating and implementing together with national and international partners a
comprehensive programme of nutritional treatment and rehabilitation (MISAU, 2010). The streamlined
plan focuses on two main target groups, children younger than 5 with either moderate or severe acute
malnourishment, and comprises three main interventions, as described in Figure 4.1. The plan also
includes separate treatment and rehabilitation routes for children older than 6, pregnant women and
for children with HIV/AIDS.
Figure 4.1
Components of the nutrition rehabilitation programme
Source: MISAU (2010)
Severely acute malnourished children (below 5 and HIV/AIDS negative) are first treated in hospital
and then in the health clinic with ready to use food supplements (Plumpy’nut). Overall the two phases
last on average 3 months (with hospitalization lasting in the order of 2 weeks). The responsibility of
these two lines of intervention falls directly under the responsibility of MISAU.
According to the plan, moderately acute malnourished children should receive a different type of food
supplementation (corn-soya-blend, CSB) to be cooked at home. The CSB programme is mainly
dependent on WFP funding and implemented directly by WFP. It is currently in the process of being
expanded from 25 to 81 districts (out of a total of 144 districts in the country). The available evidence
indicates that, in districts where CSB is not distributed, moderately malnourished children are in some
cases treated with Plumpy’nut. Overall CBS supplementation should also last on average 3 months,
although there are no administrative data to confirm the actual duration of the treatment, and there
seems to be a certain degree of flexibility in the way protocols are interpreted in the field (based
mainly on the availability of food supplements).
The coverage of the nutrition support system is reported as being low and non-uniform, partly because
of the low coverage of the health system itself and partly because of the slow expansion of the CSB
intervention. Administrative records are also extremely poor and there is undoubtedly a great deal of
underreporting in official figures. The monitoring system is extremely week and precarious, as
administrative reports are often based on incomplete and out-dated information.
As we show in Table 4.1 below, the official records available for 2009 at MISAU indicate that around
15,000 severely acute malnourished children below 59 months have been treated via either
26
October 2010
Pre-feasibility and costing analysis of three short-term social protection reform options for
UNICEF Mozambique
hospitalization or Plumpy’nut. CSB supplementation is reported for around 5,000 moderately acute
malnourished children, while around 9,000 of them received instead Plumpy’nut (possibly in districts
where the CSB programme has not been rolled out). This leads to a reported figure of 29,500 children
below 5 who received nutrition support in 2009.
We estimate that due to data gaps in the administrative records the number of children currently
served with either type of nutrition intervention is probably closer to 54,000. This figure was obtained
after accounting for inconsistencies of two main types:
Districts not reporting information. In these cases we calculate an estimated coverage on the
basis of the district population of the relevant age and the average coverage in reporting
districts within the same province.
 Districts reporting information only for a fraction of the year. In these cases the average
coverage achieved in the reported months was imputed to months with missing
information.
While the exercise of imputation is imprecise and based on some strong assumptions, in the absence
of better data this provides a rough estimate of the possible number of current beneficiaries of nutrition
support programmes in 2009. However it must also be borne in mind that the context is fluid and
figures may be changing substantially from one year to the next, amongst other reasons because of
the current expansion of the CSB programme.28
Table 4.1
Children 0-59 served and eligible for nutrition support
programmes
Eligible
Coverage
2009
2009
2009
2009
(Reported)
(Estimated)
(Projected)
Children 0-59 months
Severely
Wasted
Hospitalized (only)
6139
6139
Plumpy’nut
9213
17915
15352
24054
8914
5293
11995
14207
29559
Sub Total
Plumpy’nut (104 districts)
Moderately
Wasted (but not CSB (26 districts)
severely wasted)
Total
Treated
Sub Total
45687
52.65%
30309
100982
30.01%
54363
146669
37.07%
18314
Source: Author’s calculations on administrative records from MISAU, INE demographic projections and MICS
(2008) data.
Table 4.1 provides also some estimates of the size of the population that in principle would be eligible
to receive nutrition support programmes. This is represented by the full set of children below 5 years
who are moderately and severely acute malnourished. The MICS (2008) provides an estimate of the
proportion of severely acute malnourished children (1.30% of children aged 0-59 months) and
moderately (but not severely) acute malnourished children (2.88%). This was applied proportionally to
the population projections for children aged 0-59 months in 2009, indicating that there were around
46,000 severely wasted children and 101,000 moderately (but not severely) wasted children younger
than 5 in 2009.
Overall the coverage of current nutrition support programmes (based on projected administrative
records) should be close to 37%, with a better performance in the case of the interventions for
severely acute malnourished children, where it approaches 53%.
28
It is reassuring to see that the total estimated figure for CSB coverage in 2009 (18,000) is close to
the figure of 25,235 reported in the MISAU-UNICEF-WFP Tripartite Agreement (MISAU-PMAUNICEF, 2010), but with a longer reference period, from January 2008 to April 2009.
27
October 2010
4.3
Cost simulation
4.3.1
Static scenario
As a first step, the costing exercise for the new scheme should provide an estimate of the additional
resources that would be required to ensure full coverage of children that are already receiving nutrition
support.
The simulation is based on the following assumptions:
The transfer is provided to all children aged 0-59 months who receive any of the three
nutrition support interventions (hospitalization, Plumpy’nut or CSB).
The number of current beneficiaries estimated for 2009 (54,000) is overall correct and also
roughly applicable to the current situation.
The transfer is paid per beneficiary child, up to a maximum of 4 beneficiaries per
household.29 There are no secondary beneficiaries.
The value of the transfer is MT 100 per child and the benefit is paid on average for 9 months
(3 months during treatment and 6 months after).
At the level of coverage (for the three treatment categories) estimated for 2009 (54,000) the
simulation provides a cost estimate of MT 49 million per year, MT 22 million for severely
acute malnourished children and MT 27 million for moderately acute malnourished children.
This includes only the transfer value but excludes the administrative costs that would be
associated with the on-going operation of the project, and set-up costs.
4.3.2
Full coverage projections 2010-2014
A simple projection of the cost of the benefit scheme at full scale in 2010-2014 can be obtained, based
on estimates of the full set of eligible children who could potentially be reached by nutrition support
programmes, and therefore included in the transfer scheme.
Table 4.2 provides demographic projections of the number of children below 5, as well as an
estimation of the number of them who are likely to be moderately and severely acute malnourished.
The latter is based on a relatively strong assumption: notably that the proportion of acute
malnourished children estimated (from MICS) for 2008 will be constant over time, i.e. there will not be
substantial gains (or losses) in the nutritional status of children over the time period that is considered
for the simulation.
Table 4.2
Children 0-59 eligible for nutrition support programmes, 2010-2014
projections
Children 0-59 months
Severely Wasted
Moderately Wasted (but not severely wasted)
Total Wasted
2010
3,567,036
46,526
102,835
149,361
2011
3,631,855
47,371
104,704
152,075
2012
3,698,052
48,235
106,612
154,847
2013
3,766,034
49,122
108,572
157,694
2014
3,833,782
50,005
110,525
160,530
Source: Author’s calculations on INE demographic projections and MICS (2008) data
29
This rule is actually not imposed in the simulation, as it is very unlikely to find so many children 0-59
months receiving nutrition support in the same household.
28
October 2010
Pre-feasibility and costing analysis of three short-term social protection reform options for
UNICEF Mozambique
According to INE projections the number of children aged 0-59 months will increase by 7% during
2010-2014, bringing the total number of children below 5 to 3.8 million in 2014, of whom about
160,500 will be moderately or severely acute malnourished.
The cost simulation provides an upper bound estimate of the total envelope of resources that may
become necessary in the ideal scenario that full coverage and full take up are achieved. It shows that
if the nutrition support and the cash transfer schemes reach full scale in 2014, the total transfer cost
would reach MT 144 million (Table 4.3). Covering all eligible children in such a short time is likely to be
problematic, as it would imply in the first place increasing the number of beneficiaries nutrition support
programmes. Full coverage projections depend heavily on a substantial increase in coverage of the
health system, which is unlikely to happen at such a pace. It is therefore to be expected that, if the
scheme were implemented, there would be a more gradual expansion.
Table 4.3
Nutrition support grant: 2010-2014 cost projections
Eligible children 0-59 severely wasted
Eligible children 0-59 moderately wasted
Total transfer cost (Million MT)^
As a proportion of GDP (in 2010)
As a proportion of Tot Gvt Exp (in 2010)
As a proportion of GDP
As a proportion of Tot Gvt Exp
2010
46,526
102,835
134
0.04%
0.15%
0.04%
0.15%
2011
47,371
104,704
137
0.05%
0.15%
0.04%
0.14%
2012
48,235
106,612
139
0.05%
0.15%
0.04%
0.14%
2013
49,122
108,572
142
0.05%
0.15%
0.04%
0.13%
2014
50,005
110,525
144
0.05%
0.16%
0.04%
0.12%
Notes: ^ At 2010 constant prices. Source: Author’s calculations on MICS (2008) data, INE demographic
projections and GDP and TGE projections produced by the IMF (2009) and reported in Annex B.
29
October 2010
5
Grant for child headed households
5.1
Design considerations
Providing cash support to child headed households is the last of the three policy options for short term
expansion of social transfers identified during initial consultations. While it is extremely difficult to
quantify the number of children living in child-headed households, there is a widespread agreement
that this category often falls outside the mandate of existing programmes, and hence a more adequate
safety net would be extremely beneficial.
The evidence on child-headed households is extremely scarce, as there is no systematized and
comprehensive analysis of the causes and the types of vulnerability faced by this group. Nonetheless
the belief that the number of child-headed households may be increasing as a consequence of ongoing socio-economic and demographic processes, particularly the HIV/AIDS pandemic, seems to be
widely shared. As traditional bonds within the extended family show some signs of weakening, more
and more children may be left to look after themselves, especially in urban areas.
There is without doubt a strong case to make that child-headed households require social protection
measures. Children living in child-headed households are likely to face high exposure to a broad set of
socioeconomic risks, which in turn puts in danger their human capital accumulation and their welfare
in the long run. Still, several aspects need to be taken into account to assess the appropriateness of
social protection interventions taking the form of a cash transfer rather than other types of support.
These include:
Complementarily with traditional social welfare interventions. Children living in child-headed
households surely face such a wide set of challenges, including social, emotional and
psychological challenges, that only an integrated set of interventions can satisfy their
needs, combining a monetary or in-kind support component with social welfare services
and counselling. The design of transfer-based social protection interventions (either in
cash or in kind) would have to be conceived as part of a broader strategy, avoiding any
suggestion that it can replace service based types of support (i.e. diverting time and
resources from social workers).
Perverse incentives. Mechanisms would have to be in place to avoid incentives to form or
maintain child-headed households to obtain transfers. Any such scheme should not be a
substitute for informal social protection arrangements based on the extended family, but
rather encourage fostering. One option considered in the discussions was to extend the
monetary support to families that fostered children otherwise living in child-headed
households. This category of children is, however, hard to identify, as there is no clear
overlap between this group and other easily identifiable groups like orphans (see more
below on this point).
Use of the transfer. Providing a transfer in the form of cash may not be the most appropriate
solution if there are concerns that children may not use the money wisely, as they tackle
complex decision making options. The availability of cash may put children at risk, and
increase the likelihood of capture from other people in the community. In this respect, an
in-kind-based approach is considered by INAS to be more appropriate.
Definition of child-headed households. Extra efforts would be needed to develop a rigorous
and operational definition of child-headed households, in order to channel resources
towards vulnerable households and avoid fraud (e.g. artificial creation of child-headed
households). The definition would have to cover key dimensions like the age limit of the
head, the marital status of household members, dependence on transfers from other
households, etc. (see next section for more evidence on this). A working definition would
have to be produced on the basis of further analysis of available qualitative and
quantitative evidence on the phenomenon of child-headed households.
30
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Pre-feasibility and costing analysis of three short-term social protection reform options for
UNICEF Mozambique

Identification of beneficiaries. The identification of beneficiaries might be extremely
complex, as some child-headed households are likely to be socially excluded and highly
marginalized. In urban areas, street children should be considered as a special case for
inclusion. Significant resources, including the intensive use of skilled social workers,
would have to be devoted to the identification of potential beneficiaries and subsequent
case work. Specialized NGOs and community leaders would need to be involved at
various levels of the intervention.
Overall, the set of concerns just mentioned points towards the need to carefully review the relevance
and feasibility of such a scheme. For a number or reasons, it seems that an in-kind transfer, along with
counselling, support for fostering and other services, would be more appropriate to the needs of
children living in child-headed households. This is clearly the viewpoint of INAS, which informed us
that support for child-headed households should be provided through the existing Programa de Apoio
Social Directo (PASD), which is currently used for in-kind emergency support. At present, the PASD is
heavily under-budgeted and operates in a piecemeal and unsystematic way. The cost simulations
presented in the rest of this chapter are still useful to assess the magnitude of a scheme targeted to
such group, as per capita values can easily be provided in kind rather than as cash support.
5.2
Evidence
Providing empirical evidence on the scale and traits of the phenomenon of child-headed households in
Mozambique is a highly demanding task due to constraining data limitations. The cases captured in
standard nationally representative household surveys are likely to be so few that any inference is
deemed to be statistically non representative. Moreover, a significant sampling bias may affect the
analysis based on such sources, as the sampling framework of traditional household surveys
(including MICS and IOF/IAF) only contains households with a stable dwelling and physical address.
This automatically excludes from the reference framework some of the most vulnerable cases of
people without a fixed residence, like street children. Census data would provide a valid alternative, at
least in terms of estimating the number of child-headed households and examining the main
socioeconomic traits, though it may be affected by similar sampling bias. Unfortunately the 2007
detailed Census data were not available at the time this report was produced. A purpose specific
survey of child-headed households would probably be the best source of data for analysis, but such
information has not been collected at a sufficient scale in Mozambique.
With these data limitations in mind, the evidence presented in this section is based on the analysis of
MICS (2008) data. It should be interpreted with caution and possibly validated with Census data as
soon as this becomes available. A set of issues related to the definition of child-headed households
need to be addressed first:
The total number of child-headed households depends crucially on the definition of the age
cut-off of the head. Figures grow exponentially as the age limit is raised from 15 to 17
years old. Where the head is between 15 and 17, households are normally bigger and
there is a higher proportion of married heads.
The survey definition of “head of the household” is ambiguous. We find a not insignificant
proportion of households where the person coded as “head” in the survey is younger
than 18 but where there are also older household members (not coded as “head”) living
in the household. This may represent situations where adult members are not able to
look after the household (elderly, ill, disabled, etc.) or are non-resident members. We
restrict the definition of child-headed households to cases where there are no adult
(older) members.
 As anticipated, there are some cases, especially for households headed by children older
than 15, where the household head is (or has been) married. This reflects the common
practice of early marriage, especially for women in polygamous households. The 2008
31
October 2010
MICS found that 17% of women between the ages of 20 and 24 were married before the
age of 15 and 52% married before they were 18. We exclude married girls from the strict
category of child-headed households, as one would expect that in these cases the child
head receives some kind of support from the husband, and there is no reason to believe
that married women aged below 18 get less support than older married women.30
Table 5.1
Number of child-headed households (2010)
Total number of children 0-17
Proportion
who are registered as head of their household
and live in households with no older member
and are single (study definition)
Total number of child headed household*
Average household size
Total number of children in child headed household*
Males
5,448,951
0.228%
0.205%
0.190%
10,349
1.76
18,257
Females
5,342,786
0.247%
0.184%
0.135%
7,212
2.51
18,069
Total
10,791,736
0.237%
0.194%
0.162%
17,502
2.07
36,289
Notes: * The study definition of child-headed households excludes cases when there are older household
members in the households and the child head is not single. Source: Author’s calculations on INE demographic
projections and MICS (2008) data
Table 5.1 shows the estimates of the number of child-headed households and children living in childheaded households for 2010, based on INE demographic projections and MICS (2008) data. At a first
glance it is interesting to observe that, especially when our rather narrow definition of child-headed
households is applied (no adult household members and non-married household heads only), the
estimated number of child-headed households is as small as 17,500 for the 0-17 group. This may be a
result of the sampling bias mentioned earlier. Based on previous Census reports, one can estimate
that there should be in reality between two and three times more child-headed households than those
estimated using MICS.31 Still, even assuming a large margin of underrepresentation, these figures
indicate that child-headed households are a very small group in Mozambique and the scale of a social
protection transfer scheme would hence be rather limited.
It is also interesting to note the clear gender dimension of the data reported in Table 5.1. Young
females seem to be left on their own more often than males of the same age. On the contrary older
female head of households are more likely to be married, and hence fall outside our definition of childheaded households. In all cases girl heads of households tend to live in bigger households, as
possibly they are left with the responsibility of looking after their younger siblings.
A final point regards the overlap between the category of children living in child-headed households
and orphan status. In Table 5.2 we show that, despite what one would expect, the overlap is minimal.
The MICS data (2008) suggest that parents of children living in child-headed households are in most
cases still alive.32 These results should be interpreted with caution, as they may be affected by
30
We also exclude girls in civil union.
31
Or anecdotal evidence from NGOs suggests there may be around at least 3,500 street children in
Maputo (2005) and 5,000 across the country (2002).
32
There should be a missing data concern here. However there are very few cases of no response,
and “don’t know” responses are coded as if the parent were dead.
32
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Pre-feasibility and costing analysis of three short-term social protection reform options for
UNICEF Mozambique
Table 5.2
Orphans and child-headed households
Proportion of children (0-17) in child headed household*
who are orphan of 1 parent
who are orphan of 2 parents
Proportion of orphans of 1 parent
who live in a child headed household*
Proportion of orphans of 2 parents
who live in a child headed household*
17.1%
0.6%
0.3%
0.1%
Notes: * The study definition of child-headed households excludes cases when there are older household
members in the households and the child head is not single. Source: Author’s calculations, MICS (2008)
Orphans are indeed a much broader category than children in child-headed households, as we show
in Table 5.3, where we estimate that in 2010 there are around 183,303 orphans of both parents
younger than 18 in Mozambique.33
Table 5.3
Number of orphans (2010)
0-17
Proportion of children
who are orphan of 1 parent
who are orphan of 2 parents
Number of orphans of 1 parent
Number of orphans of 2 parents
12.676%
1.699%
1,394,417
183,303
0-14
11.163%
1.368%
1,039,694
127,395
Source: Authors calculations, MICS (2008)
5.3
Cost simulation
5.3.1
Full coverage projections 2010-2014
While in the previous two chapters a static cost estimate was provided, based on existing
interventions (PSA and nutrition support), this approach does not apply to the transfer for child-headed
households as there in no pre-existing programme in place. One possible approximation is to estimate
the cost that such a transfer scheme would have if all children in eligible households were covered
and take-up were complete. This again represents an upper bound estimate of the total possible
expenditure at scale.
The cost simulation is based on the following assumptions:
The transfer is provided to any child head of household (narrowly defined as indicated
above) and any younger household member, up to a maximum of 4 beneficiaries per
household.34
The value of the transfer is MT 100 per child per month or the equivalent of MT 1,200 per
year. All children living in a child-headed household are entitled to receive the same
amount.
The grant is paid for the whole duration of the year and until the head of the household is
older than 17.
33
Based on INE population projections and MICS 2008 prevalence rates.
34
This rule is actually not imposed in the simulation, as it is almost impossible to find so many children
in the same child-headed household.
33
October 2010
Although this option is presented as a cash transfer, it could be provided in kind, the
assumption being that the value would be the same, i.e. equivalent to MT 1,200 a year per
child. No allowance has been made in the simulation for the provision of support services
(counselling, assistance for fostering or other support), although this would be equally
important for these children, quite labour-intensive (in terms of social workers’ time) and
possibly more costly than the transfers. This is a major limitation of the cost simulation
provided, which is limited to a single narrow intervention.
A forecast of the number of eligible children in child-headed households in 2010-2014 was produced
on the basis of INE population projections and assuming that the proportion of children in childheaded households (very likely to be under-estimated from MICS, 2008) is constant over time. The
results are presented in Table 5.4.
Table 5.4
Grant for children in child-headed households, 2010-2014 cost
projections
2010
Eligible children 0-17 in households whose
head is younger than 18
Total transfer cost (Million MT)^
As a proportion of GDP (in 2010)
As a proportion of Tot Gvt Exp (in 2010)
As a proportion of GDP
As a proportion of Tot Gvt Exp
36,289
44
0.01%
0.05%
0.01%
0.05%
2011
36,991
44
0.01%
0.05%
0.01%
0.05%
2012
37,713
45
0.01%
0.05%
0.01%
0.04%
2013
38,459
46
0.01%
0.05%
0.01%
0.04%
2014
39,237
47
0.01%
0.05%
0.01%
0.04%
Notes: ^ At 2010 constant prices. Source: Author’s calculations on MICS (2008) data, INE demographic
projections and GDP and TGE projections produced by the IMF (2009) and reported in Annex B.
At the level of full coverage projected for 2014 the simulation provides a cost estimate of MT 47 million
per year for a grant scheme targeted to households with heads younger than 17. This includes only
the transfer value but excludes the administrative costs that would be required for the on-going
operation of the programme, and set-up costs.
As previously mentioned, the small magnitude of the group of child-headed households must be
considered carefully, as launching such a small scale social transfer programme may not result in
efficient management and administration. Economies of scale can probably not be achieved, while a
cumbersome administrative machine may need to be put in place to identify, enrol and make transfers
to the beneficiaries, who may be scattered widely in geographical terms. This may in fact be justified
and unavoidable, although probably for in-kind rather than cash transfers, along with psychosocial
counselling and support for fostering, which is a high priority for these highly vulnerable children.
34
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Pre-feasibility and costing analysis of three short-term social protection reform options for
UNICEF Mozambique
Bibliography
Hodges, Anthony, and Luca Pellerano (2010) Development of Social Protection: Strategic
Review for UNICEF Mozambique, Oxford Policy Management, October
INAS (2009) Relatório de Actividade de Apoio ao Programa Subsídio de Alimentos (PSA) do
Exercício Económico de 2008, Instituto Nacional de Acção Social, Maputo
INAS (2010) Relatório de Actividade de Apoio ao Programa Subsídio de Alimentos (PSA) do
Exercício Económico de 2009, Instituto Nacional de Acção Social, Maputo
INE (2009) Inquérito de Indicadores Múltiplos 2008, Sumário, Instituto Nacional de
Estatística, Maputo
IMF (2009) Republic of Mozambique: Fifth Review under the Policy Support Instrument, First
Review under the Twelve-Month Arrangement under the Exogenous Shocks Facility, and
Request for Modification of Assessment/Performance Criteria – Staff Report and Press
Release, IMF Country Report No. 09/327, International Monetary Fund, Washington,
D.C., December
MISAU (2010) Manual de Tratamento e Reabilitação Nutricional, Departamento de Nutrição,
Ministerio da Saude, Maputo
MISAU-PMA-UNICEF (2010) Acordo tripartido. Programa de Reabilitação Nutricional
Componente de Suplementação Alimentar, Maputo
MMAS (2009b) Nota Técnica de Suporte à Proposta de Estratégia Nacional de Protecção
Social Básica, Ministério da Mulher e da Acção Social, Maputo
MMAS (2010) Plano Operacional de Acção Social Directa 2010-2011, Instrumento de apoio
na operacionalização e custeamento da Estratégia Nacional de Segurança Social Básica
2010-2014, draft, Ministério da Mulher e da Acção Social, Maputo, August
Soares, Fabio, Guilherme Hirata & Rafael Perez Ribas (2009) Evaluation of the Programa
Subsídio de Alimentos in Mozambique: An Analysis of the Baseline Survey, October
República de Moçambique (2010) Estratégia Nacional de Segurança Social Básica 20102014 (ENSSB), Maputo, March
35
October 2010
Annex A
Detailed methodology for PSA long term cost
projections
a) In order to estimate the number of elderly-headed households who would be eligible to the PSA
(before income test) we preceded with the following steps:
Construct from INE projections population estimates for men older than 59 and women older
than 54 from 2010 to 2014.
Calculate from existing survey data (MICS, 2008) the probability that an elderly person in the
relevant age cohort is head of the household. This is based on the understanding that
only elderly household heads are eligible to receive the PSA.35 This probability is
estimated separately for men and women cohorts.
Estimate the number of elderly headed households in every year by applying this constant
proportion (probability) to the relevant population cohort. The underlying assumption is
that the relative distribution of types of households is fixed and constant over time.
b) At this stage some economic criterion for eligibility must be imposed on the set of elderly-headed
households (something that was not done in the simulations included in the ENSSB) in order to
identify the set of eligible households who would fall under this threshold. This is done in two steps
Calculate from existing survey data (IAF 2002/03) the probability that an elderly-headed
household has a monthly income per capita below the inflation adjusted equivalent of MT
100, which is the economic eligibility criterion provided in the existing PSA Procedures
Manual. This is based on the assumption that the income threshold is actually enforced
in the field according to the manual regulations.
Estimate the number of income eligible elderly-headed households in every year by applying
this constant proportion (probability) to the relevant population cohort. The underlying
assumption is that that the relative income distribution of elderly-headed households is
constant over time. This may be considered a rather strong assumption, as it ignores
poverty dynamics between 2003 and 2014, but in the absence of the microdata from the
most recent household budget survey (IOF 2008/09), the data from the 2002/03 IAF
provide the best possible approximation.
c) In order to estimate the number of eligible child dependents in eligible elderly-headed households
we preceded with the following approach:
Construct from INE projections population estimates for children of different cohorts (mainly
0-14 and 0-17) from 2010 to 2014.
Calculate from existing survey data (MICS, 2008) the probability that a child in the relevant
age cohort lives in an elderly-headed household.
Estimate the total number of eligible children living in an elderly-headed household in every
year by applying this constant proportion to the relevant population cohort. The
underlying assumption is that the relative distribution of children living in different
household types is fixed and constant over time.
Calculate the average number of children in an elderly-headed household, using as a
denominator the number of elderly-headed households estimated at step a).
35
The results do not change substantially if a looser categorical criterion is applied, considering all
elderly household members. In fact a relevant proportion of households with elderly members but
headed by non-elderly fail the income test.
36
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Pre-feasibility and costing analysis of three short-term social protection reform options for
UNICEF Mozambique
Adjust the average number of children per household for the fact that the maximum number
of dependent beneficiaries is 4, based on the ratio between average number of children
below and above the threshold in existing survey data (MICS 2008).
Estimate the total number of eligible children living in eligible elderly-headed households by
multiplying the adjusted average number of children per household calculated in the
previous point for the number of eligible elderly-headed households estimated at step a).
This assumes that the average number of children per household is the same for eligible
and non-eligible households, which seems to be reasonable based on the low correlation
between eligibility and poverty status (see also footnote 17).
d) In order to estimate the number of eligible other dependents in eligible elderly-headed households
we follow a simple approach:
Estimate the average number of eligible dependents by type in elderly headed households
(MICS, 2008).
Estimate the total number of eligible dependents living in eligible elderly-headed households
by multiplying the average number of eligible dependents by the number of eligible
elderly-headed households estimated at step a). This assumes that the average number
of non child dependents per households is fixed over time.
37
October 2010
Annex B
Table B.1
Macroeconomic and public finance framework
Government finances (as % of GDP), 2008-2014
Actual
Projections
2008
2009
2010
2011
2012
2013
2014
240
260
300
337
378
425
479
16.0
16.4
16.7
17.1
17.6
18.0
18.3
9.4
9.3
10.8
10.9
10.9
10.9
10.9
Expenditure and net lending
27.9
30.1
31.4
30.8
31.0
31.5
31.8
Domestic primary balance before
grants
-3.4
-5.3
-5.1
-3.6
-3.6
-3.6
-3.5
Overall balance (after grants)
-2.3
-4.3
-3.9
-2.7
-2.5
-2.5
-2.5
Net aid flows
12.9
12.2
12.6
12.9
12.8
12.8
12.7
Gross aid flows
13.4
13.0
13.4
13.6
13.6
13.6
13.6
5.5
5.1
4.3
4.4
4.4
4.4
4.4
GDP at current prices (MT billion)
Government finances
Revenue
Grants
Budget support (gross)
Source: IMF (2009).
38
October 2010