Targeting Indicators of Social Aid in Mauritius
Christophe Muller
Consultant
United Nations Development Programme
June 2010
Acknowledgments
I am grateful to many executives and agents from the Ministry of Social Security, the
Central Statistics Office, the Ministry of Finance and Economic Development, the
UNDP and the European Union for their support. I particularly thank Mr. Veerasamy
(Permanent Secretary), Mr. Soobratty, Mr. Fatadin, Mrs. Jugganaikloo, Mrs. Ramjane,
Mr. D. Bundhoo, Ms. S. Bhugaloo, Ms. Rucktooa, Mr. Deerpalsing, Mrs. Leyla Tegma
Reddy and Mr. Cuvillier for their efforts that contributed to the success of the mission.
Executive Summary
In this document, we present estimation results of targeting statistics for the Social Aid
programme of Mauritius.
Only 5.13 % of poor households (in the sense of the LCS general poverty notion) are
covered under social aid. However, because the non-poor are much more numerous
than the poor, we find that 89.8 % of the covered households by social aid are non-poor:
a very large leakage rate. Similar calculation were carried out when considering the
nutritionally (extreme) poor households instead. They yield higher leakage statistics
(97.7 %) and very low coverage (3.27%). At household level, the performance of the
social aid programme is thus dramatically bad.
On the whole, the targeting performance of social aid, Trust Fund (TFSIVG) and
Housing Fund (NHDC) programmes is slightly better at the individual level. This is
because poor households have a larger household size than the average. However, the
coverage of the poor by social aid remains extremely mediocre. The coverage rate of
social aid is 5.3 % for general poverty and 3.9 % for nutritional poverty. It is all the
1
more insufficient that only 4.58 % of the population is under social aid. So, the
coverage of the poor by social aid is only slightly better than the coverage of the whole
population. Very little anti-poverty targeting orientation characterizes the social aid
programme.
Respectively, the coverage statistics at household level for the TFSIVG and the NHDC
are, for the general poor: 2.5 % and 1.4 %; and for the nutritional poor: 1.3 % and
even 0 %. Clearly, even if the performance of the social aid programme is not
satisfactory, it would be illusory to think that its role could be played by these two other
funds, which have still worse coverage properties.
Finally, the huge majority of persons under social aid are actually non-poor (91 % for
general poverty; 97 % for nutritional poverty).
Although, the Social Aid, TFSIVG and NHDC programmes are interesting cases to
study, they are only some of the possible programmes that can be assessed using the
methods developed in this report. In particular, alternative social programme designs
will be testable by using the same methodology.
2
Table of Contents
1. Introduction
4
2. Targeting
7
3. Estimators
12
4. The Matched Data
14
5. The Estimation Results
17
Appendix A1: References
25
Appendix A2: Persons Met during the Mission
27
3
1. Introduction
The Social Registry of Mauritius has been designed as a large database of potential and
actual social programme beneficiaries. It will assist the Ministry of Social Security
(from now ‘The Ministry’ or MSS) in identifying the beneficiaries of each programme
and deciding the level of assistance for each beneficiary. It will also be used to assess
social programmes and improve their performances.
The project of the ‘Social Registry of Mauritius’ (SRM) is under the responsibility of
the Ministry. In this project, the Ministry is supported by the Ministry of Finance and
Economic Development (MOFED) and by the Central Statistics Office (CSO). The
United Nations Development Programme is providing technical assistance.
The objectives of the project are:
-
Managing the social programmes in an integrated way;
-
Enhancing the targeting performance of programmes;
-
Designing a unified approach to welfare and targeting criteria;
-
Analysing cyclical and structural poverty reduction policies.
The database under building will replace the imperfect electronic files currently used by
the Ministry of Social Security (MSS). It will be used for a variety of purposes, not only
for payment of allowances to beneficiaries, but also for economic analyses, anti-poverty
monitoring, and establishment of proxy-means transfers schemes that would improve
the targeting efficiency of Social Aid and other social programmes.
4
Thus, the Mauritian government is taking the lead in modernizing its social system
around the social databases of the country. This is made possible by first emulating
statistical and organisational methods used in industrial economies, such as in European
countries with a well-developed social system, but also by taking advantage of the
sophisticated methods that have been developed for fighting poverty in developing
economies.
The SRM will also bear important consequences for assessing the budgetary burden of
social systems and social reforms. Last but not least, it should allow substantial savings
of public funds by improving the efficiency of social transfers in the country.
In this report we define and estimate targeting indicators for the Social Aid programme
in Mauritius.
In Section 2, we discuss the notion of targeting. In Section 3, we present the estimators.
In Section 4, we mention the procedure for generating the matched data. Finally, we
report the estimation results in Section 5.
5
Abbreviations:
CSO: Central Statistics Office.
CS: Complementary Survey.
The Ministry or MSS: The Ministry of Social Security.
UNDP: United Nations Development Programme.
SRM: Social Registry of Mauritius.
MOFED: Ministry of Finance and Economic Development.
LCS: Living Conditions Survey.
DCP: Decentralized Cooperation Programme.
EU: European Union.
TFSIVG: Trust Fund for the Social Integration of Vulnerable Group.
NHDC: National Housing Development Company.
6
2. Targeting
2.1. The notion of targeting
Transfer schemes are popular policy tools against poverty. The Social Aid programme
of Mauritius is one of these schemes that provide cash transfers which are assistance in
cash to the poor or to those who face a risk of falling into poverty. In that sense, they are
seen as ‘targeting’ on poverty.
The performance of anti-poverty transfer schemes in the world is quite variable (Coady,
Grosh and Hoddinot, 2004). Public transfers may be efficient for redistributing income.
However, they are generally found not to be so. In particular, the poor households or the
target population are often found to be badly covered by these schemes. In order to
describe these inefficiencies, targeting indicators must be defined.
7
2.2. Targeting indicators
With imperfect targeting, only people among the poor who are predicted as poor
can benefit from poverty alleviation. On the other hand, non-poor people predicted as
poor receive transfers. Thus, accordingly to the two types of targeting errors, two
indicators, Leakage and Coverage, are popular for measuring targeting performance.
The Type I error (Undercoverage = 100% - Coverage) is that of failing to reach some
members of the targeted group. It is estimated by the probability of not receiving any
transfer, while poor. The Coverage indicator is just its complement to 100%, i.e. the
probability of being covered while poor. The Type II error arises where benefits are
awarded to ineligible people under perfect targeting. The Leakage is an assessment of
this error. The leakage of the transfer programme is the percentage of households or
persons who receive transfer and whose pre-transfer living standard is above the poverty
line. Another possible indicator of target efficiency is the share of transfers going to the
targeted population.
The undercoverage statistics provide a simple way of describing the risk for a
poor not to be reached by the transfer programme. The undercoverage rate for N
individuals can be written as
UR = ( (1/N) Ni=1 I[t(Xi ) = 0] I[yi < z] ] )/ P0
where P0 
1
N
N
I
i 1
[ yi  z ]
, yi is the living standard of person i, t(Xi) is the transfer
received by person i of characteristics Xi, I[.] is the dummy variable operator.
That is: UR is, amongst households who are poor, the proportion of individuals
who do not receive any transfer. The coverage statistics is just C = 1 – UR, i.e. the
proportion of the poor who are covered. It can therefore be written as
C = ( (1/N) Ni=1 I[t(Xi ) > 0] I[yi < z] ] )/ P0
8
The indicator of Coverage and Leakage can also be calculated at household level,
if the interest is to target households rather than individuals.
Coady and Skoufias (2004) suggest also using the Distribution Characteristic
Indicator (DCI), which shows the change in social welfare marginal benefit achieved by
transferring a standardized budget to the programme.
The DCI can be defined from the optimization of a Social Welfare function
W(…, Vh(p, mh),…) under the budget constraint ∑h mh ≤ B, where B is the total budget
for transfers, mh is the amount transferred to individual h, p is the price vector, Vh is the
indirect utility of individual h and W is an aggregating function for the society social
welfare.
Under typical anonymity and concavity conditions, the solution of this problem
is characterized by following first-order differential describing the effect of changing
transfers marginally:
dL = ∑h bh dmh- λ ∑h dmh
where bh represents the marginal social evaluation of extra-income to individual h,
λ is the Lagrange multiplier of the budget constraint in the optimisation problem and L
is the Lagrange function.
One can measure the marginal social efficiency of any programme j with its
corresponding DCIj = ∑h bh dmh / ∑h dmh = ∑h bh θh
where θh is the share of the total transfer budget received by individual h.
9
In practice the coefficients bh are calculated as Atkinson’s weights bh = (yh/y0)ε
for a list of values for parameter ε (e.g. ε = 1, 2, 5) and y0 a living standard of reference.
For our application, we use y0 as the mean living standard.
Coady and Skoufias propose to decompose the DCI as DCI = targeting
efficiency + redistributive efficiency
= ∑h bh dm* / ∑h dmh + ∑h bh (dmh – dm*)/ ∑h dmh,
where dm* is the average transfer to beneficiaries. Again, all these measures can be
defined at household level rather than individual level.
The interest of using the DCI is to introduce some concern about the relative
individual living standards, rather than just separating abruptly poor and non-poor
individuals. It therefore corresponds to aversion for inequality. The strength of this
aversion is measured by parameter ε.
10
3. Estimators
Most targeting indicators can be written as
T =  t(y , X , z) dFXy (X , y) /  k (y , z) dFy (y),
where t is the kernel function describing the targeting intensity based on characteristics
X for living standard y with poverty line z, k is the kernel function defining the
normative importance of individuals to target (e.g., the poor), and FXy and Fy are the cdf
of characteristics and living standards of individuals. For example, the two kernel
functions t(y, X, z) and k(y, z) for the Coverage indicator are:
t(X, y, z) = I[t(X) > 0].I[y < z] and k(y, z) = I[y < z].
So, the sampling estimation problem can be seen as that of estimating ratios of
means and the sampling standard errors of these ratios.
The basic sampling estimator of the above generic targeting indicator T is the
following, based on ratios of the classical Horwitz-Thompson sampling estimator of the
total:
11
n
W
Tˆ = s=1 n
s
HHS s t( X , y s , z)
 HHS
,
s
k ( y s , z) s
s=1
where Ws is the HBS sampling weight of surveyed household s (s = 1,...,n), HHSs is its
HBS household size, ys is the household living standard and z is the poverty line.
Different poverty lines are used for households belonging to Mauritius and Rodrigues.
Note that, in the formula of T, one uses the cumulative distribution function of
personal living standards, while only households are observed. This is what implies
weighing the functions t(X, y, z) and k(y, z) in the integrals, by the household size.
For Coverage and Leakage estimators, the formulae simplify as they boil down
to estimating percentages in sub-populations of interest. In the next section, we discuss
the data on which the estimations are based.
12
4. The Matched Data
4.1 The principle of the matching
Data linking procedures are commonly used to match data from different sources
thereby enabling a more effective use of the data, improving data quality and reducing
the need for further data collection.
Matching data sets is consistent with keeping the cost of household surveys to an
acceptable level. This outcome is obtained by exploiting administrative data sources.
Indeed, when useful data is available in administrative sources (such as that of the SRM
database), there is no reason to collect this information again.
In our specific case, matching together HBS data, LCS data and CS data enables
us to achieve several objectives. First, the HBS data provides representativeness to
targeting estimates, through the use of the sample of LCS households that have been
matched with the CS data. Second, for this small sample, all the variables available in
HBS, LCS and CS can be used in analysis. In particular, for the corresponding
households, we simultaneously know their living standards (in 2006/07 though) and the
amount of benefits received from the Social Aid programme. This conjunction of
information allows us to estimate targeting estimators in terms of the capacity of the
social aid programme to reach the poor.
13
4.2 The matching procedure
In order for existing data sources to be used in conjunction with a survey,
records in the survey need to be matched to corresponding records in the administrative
source using data linking procedures. For individuals in the SRM data, this was done by
first matching households using the names of the household head, the address and other
household demographic characteristics. Much data checking and cleaning was necessary
before the matching techniques could be applied. In the UK, it was found that
generating data linkages using matches on sex, data of birth, and post-code was
relatively accurate. However, using such variables only for matching households in
Mauritian social databases was found not so satisfactory. In the future, National Identity
Numbers should be used for this type of operations in Mauritius.
The confidentiality of the collected data is guaranteed by CSO and MSS that
officially ensure that the data will be used only for the statistical purposes for which
consent has been given. Moreover, disclosing identifiers, such as names and addresses,
should be excluded from the matched data set. Finally, after a given time period, these
data should be destroyed when they are no longer useful.
The Complementary Survey (CS) data for Social Aid beneficiaries was matched
with the Living Conditions Survey (LCS) data in order to identify households who are
in both data sets. The main issue encountered during the matching procedures was the
lack of an identifier common and unique to respondents in both data sets. This was due
to the following two main reasons:
(a) Information on the National Identity Card number was collected only for the
Complementary Survey;
14
(b) The surnames and names in the Complementary Survey were those that
appeared either on the National Identity Card or the Birth Certificate of the
respondent, whereas for the Living Conditions Survey, the surnames and names
were recorded from the verbal answers of respondents. Moreover, in many cases,
the first name of a respondent recorded in the Complementary Survey (which is
the respondent’s official first name), is not the same as the first name recorded in
the Living Conditions Survey.
In the absence of a unique common identifier, the following matching
procedures were adopted to minimize the exclusion of matched households:
(c) A first matching was conducted using surnames and names as common
identifiers. This first step identified all matched cases where the surnames and
names were written exactly in the same way in both datasets;
(d) A second matching was conducted using surnames and district of residence as
common identifiers. Members of the matched households were then compared
with other variables such as first names, gender and age.
Unfortunately, this procedure led to a small percentage of household bypassed
during the matching. We use an adjustment coefficient to correct this shortcoming.
We now turn to the estimation results.
15
5. The Estimation Results
5.1 The Coverage and Leakage estimates
In this document the targeted population are the poor. They are defined in terms
of living standards below or above the poverty lines. The poverty lines are defined in
Muller (2009a, 2009b).
The living standard variable used in the targeting estimates corresponds to years
2006/07, the HBS data period, which is consistent with the estimated nutritional poverty
line for Mauritius also based on HBS data. The general poverty lines are based on the
2006/07 Household Budget Survey for food basic needs estimated using the 2008
Living Condition Survey, deflated to the 2006/07 period.
The general poverty line estimates are absolute poverty lines corresponding to
state-of-the-art techniques used for poverty statistics. Namely, basic needs benchmarks
are used that correspond to the opinions of the most consistent respondents in the
Mauritius population.
Let us first have a look at the number of social aid beneficiaries. In the CS,
12,827 households have been registered under the social aid programme. Since the
number of households at the same period is roughly estimated at about 360,000
households, we obtain in the whole population of households a rate of households under
social aid that is equal to: 12,827 / 360,000 = 3.56 %.
16
However, using the 46 observations of beneficiary households found in the
matched data, out of 2125 LCS households, we obtain a corresponding rate of only
2.16 %, which barely reaches 2.57 % when applying the LCS sampling weights. A
quick estimates of the sampling standard errors yields an upper bound of the sampling
confidence interval equals to 2.57 % + 2*sqrt(0.0257*(1-0.0257)/sqrt(2125) = 3.24 %,
which is probably an overestimate since no stratification has been included in the
calculation. However, there is still some ground to cover to find the above 3.56 %
obtained from direct counts in the SRM database.
These reflexions suggest that systematic underestimation occurred when
generating the matched data. One possibility is that new households emerged that were
not surveyed at the time of the HBS and thus entered after HBS collection time in the
social aid programme. However, the rate of new households being probably less than 1%
or 2% of the total number of beneficiaries, this would account only for a very small part
of the gap. So, the most likely reason of the gap seems to be that the matching of LCS
and CS data was imperfectly performed and that some households escaped the matching
procedure. This is quite possible if different names were used to record household
members in the CS data and for the HBS-LCS surveys.
Still the underestimation is not devastating, in particular when compared with
naive direct questions in HBS about social aid coverage that yield a percentage of
households under social aid close to 1.30%, a much too low figure. The latter result is
not surprising. Indeed, it is well known by survey statisticians that asking questions
about transfers in household surveys do not generally provide exploitable figures.
17
Therefore, we decided to pursue with the exploitation of the matched data, by
just implementing a correction ratio of 3.56/2.57 = 1.38, for targeting indicators that are
expressed in the form of percentages.
Under these hypotheses we obtain the statistics reported in the following tables.
The first table presents targeting indicators in terms of households, the second table in
terms of individuals. We first briefly discuss the targeting results at the household level.
Only 5.13 % of poor households (in the sense of the LCS general poverty notion)
are covered under social aid. However, because the non-poor are much more numerous
than the poor, we find that 89.8 % of the covered households by social aid are non-poor:
a very large leakage rate. Similar calculations were carried out when considering the
nutritionally (extreme) poor households instead. They yield higher leakage statistics
(97.7 %) and very low coverage (3.27%). At household level, the performance of the
social aid programme is thus dramatically bad.
The other studied programmes, TFSIVG and NHDC, have also poor targeting
properties, with still lower coverage of the poor than social aid.
18
Standard errors are in parentheses. In this report the standard errors are
calculated by neglecting all preliminary stratification and clustering to simplify a very
complex sampling situation. Given all the stratification done in the succession of the
used surveys, the calculated standard errors should overestimate actual sampling errors.
However, targeting indicators are better measured in terms of individuals than
households. Conveniently, all the above targeting indicators can be estimated by
counting individuals rather than households. We now discuss these statistics.
On the whole, the targeting performance of social aid and the two other social
programmes is slightly better at the individual level. This is because poor households
have a larger household size than the average. However, the coverage of the poor by
social aid remains extremely mediocre. The coverage rate of the poor by social aid is
5.3 % for general poverty and 3.9 % for nutritional poverty. It is all the more
insufficient that only 4.58 % of the population is under social aid. So, the coverage of
the poor by social aid is only slightly better than the coverage of the whole population
by social programmes. Very little targeting orientation characterizes the social aid
programme.
Respectively, the coverage statistics at household level for the TFSIVG and the
NHDC are, for the general poor: 2.5 % and 1.4 %; and for the nutritional poor: 1.3 %
and even 0 %. Clearly, even if the performance of the social aid programme is not
satisfactory, it would be illusory to think that its role could be played or complemented
by these two other funds, which have still worse coverage properties.
19
When turning our attention to the Leakage statistics, that is: to the transfers
wasted because they are given to the non-poor, we find another dire picture. The huge
majority of persons under social aid are actually non-poor (91 % for general poverty;
97 % for nutritional poverty). High leakage rates are also found for the TFSIVG and the
NHDC, although this is less an anomaly as the latter funds should not necessarily be
targeted to the poor.
20
Tables on Targeting Indicators
Households:
%
Households
Covered
Coverage
(general
poverty)
Leakage
(general
poverty)
Coverage
(nutritional
poverty)
Leakage
(nutritional
poverty)
Social Aid
2.63
(1.00)
5.13
(1.80)
89.85
(4.2)
3.27
(1.64)
97.77
(1.29)
TFSIVG
1.11
(.345)
2.56
(.93)
83.54
(6.53)
3.04
(1.78)
93.24
(4.04)
NHDC
1.00
(.70)
.51
(.51)
96.41
(4.32)
0
(NA)
100
(NA)
%
Population
Covered
Coverage
(general
poverty)
Leakage
(general
poverty)
Coverage
(nutritional
poverty)
Leakage
(nutritional
poverty)
Social Aid
4.58
(1.24)
5.32
(1.89)
91.23
(3.64)
3.86
(2.08)
97.12
(1.76)
TFSIVG
1.42
(.49)
2.55
(0.99)
86.44
(6.14)
1.36
(.49)
93.23
(4.19)
NHDC
1.36
(.93)
1.43
(1.00)
97.17
(3.4)
0
(NA)
100
(NA)
Individuals:
Standard Errors in parentheses.
21
5.2 Caveats
There are a few caveats to mention so that the reader is well aware of the limit of
the shown statistics when interpreting them. First, the HBS data on income and
consumption expenditure refer to period 2006/07, whereas the CS data refer to end 2008.
The produced statistics are therefore a synthesis of the best available data, while this is
at the cost of the time consistency of the used information. Then, slightly different
figures would have been obtained if data for the same year, say 2008, had been
available.
Second, the post-stratification techniques used for the sampling estimators
applied to LCS data do not completely correct for a slight sampling bias in LCS. This
minor issue is originated from an unbalanced drawing of households for LCS.
Third, as mentioned before, a small proportion of households have been missed
at the stage of the data matching. This issue has been corrected by applying a global
correction coefficient. However, if the characteristics of the missed households are
special, some slight bias may occur.
Fourth, we do not have any usable data on Rodrigues for social aid. Our analyses
are therefore limited to the case of the island of Mauritius only.
Fifth, the data in the CS only cover the ‘regular beneficiaries’ of the social aid
programme. We therefore limit our analyses to this sub-section of the programme. On
the whole, we consider that these issues are not serious enough to change the face of the
results that have been obtained.
22
5.3 Distribution Characteristics Indicators
DCI for the Social Aid Programme
DCI
Targeting Efficiency
Redistribution Efficiency
Epsilon =1
.664
.648
.016
Epsilon =2
.509
.484
.025
Epsilon =5
.547
.493
.053
Epsilon =1
.658
.639
.018
Epsilon =2
.498
.470
.027
Epsilon =5
.515
.461
.054
Household
level:
Individual
level:
The estimates of DCI show a relatively high efficiency when considered from the
point of view of global social welfare. This efficiency is mostly due to the component
denoted targeting efficiency, and little to the redistribution efficiency. This means that
the Social Aid Programme mostly contributes to the global social welfare by selecting
households and individuals who are relatively less well-off than average. However, the
specification of the amount transferred does not favour much redistribution.
23
Appendices
A1: References
Atkinson, A. B. (1995). “On Targeting Social Security: Theory and Western Experience
with Family Benefits” in Dominique van de Walle and Kimberly Nead (eds.), Public
Spending and the Poor, Theory and Evidence. Pp. 25-68. The Johns Hopkins
University Press for the World Bank.
Baker, J. and Grosh, M. E. (1994). “Poverty Reduction Through Geographic Targeting:
How Well Does It Work,” World Development, vol. 22: 983-995.
Besley, T. and Kanbur, R. (1993). “Principles of Targeting,” in Michael Lipton and
Jacques van der Gaag (eds.), Including the Poor, Pp. 67-90, The World Bank,
Washington, D.C.
Bigman, D. and Srinivasan, P.V. (2002). “Geographical Targeting of Poverty
Alleviation Programs: Methodology and Application in Rural India,” Journal of Policy
Modelling, 24, 237-255.
Coady, D.P., M. Grosh and Hoddinott, J. (2004). “Targeting Outcomes Redux,” World
Bank Research Observer, vol. 19, No. 1, 61-85, Spring.
Coady, D.P. and Skoufias, E. (2004). “On the targeting and redistributive efficiency of
alternative transfer instruments,” Review of Income and Wealth, Ser. 50, No. 1, 11-27,
March.
Cornia, G. A. and Stewart, F. (1995). “Two Errors of Targeting,” in Dominique van de
Walle and Kimberly Nead (eds.), Public Spending and the Poor, Theory and Evidence.
Pp. 350-386. The Johns Hopkins University Press for the World Bank.
Datt, G. and Ravallion, M. (1993). “Regional Disparities, Targeting and Poverty in
India,” in Michael Lipton and Jaques Van Der Gaag (eds.), Including the Poor, pp. 91114. The World Bank, Washington, D. C.
Deerpalsing, N. (2008), “Revised income support scheme,” mimeo MSS.
Muller, C. (2009a), “A Nutritional Poverty Profile in Mauritius 2006-07,” UNDP
Mauritius, Port-Louis, January.
Muller, C. (2009b), “Living Condition Survey in Mauritius: General Poverty Profile,”
UNDP Mauritius, Port-Louis, October.
Muller, C. And S. Bibi (2010), “Refining Targeting against Poverty: Evidence from
Tunisia”, (with S. Bibi), Forthcoming Oxford Bulletin of Economics and Statistics.
24
Park, A., S. Wang , and Wu., G. (2002). “Regional Poverty Targeting in China,”
Journal of Public Economics, vol. 86, pp. 123–153.
Ravallion, M., (1992). “Poverty Alleviation Through Regional Targeting: A case Study
for Indonesia,” in Avishay Braverman, Karla Hoff, and Joseph Stiglitz (eds.),
The
Economics of Rural Organization. Oxford University Press for the World Bank.
Schady, N. R. (2002). “Picking the Poor: Indicators for Geographic Targeting in Peru,”
Review of Income and Wealth, Serie 48, No. 3: 413-433, September.
Weiss, J. (2005). “Poverty Targeting in Asia,” Edward Elgar, Cheltenham, United
Kingdom.
Wodon, Q. (1997), “Targeting the poor using ROC curves,” World Development, Vol.
25(12), 2083-2092, December.
25
A2: Persons met during the mission
NAME
DESIGNATION
Institution
Mr. A.Veerasamy
Permanent Secretary
MSS
Ms Pratima Bungaroo
Assistant Secretary
MSS
Mr. F Fatadin
Commissioner, Social Security
MSS
Mrs. Leyla Tegma Reddy
UNDP Resident Representative
UNDP
Mr. Emmanuel Cuvillier
UN Lead Economist
UNDP
Mrs. Magda Verdickt
UN Economist
UNDP
Mr. Satchuda Arnachellum
Programme Officer
UNDP
Mr. Dayachund Bundhoo
National Consultant
UNDP
Ms Mitheela Rucktooa
National Consultant
UNDP
Mrs. Naseem Ramjan
Acting Statistician
CSO
Mr. A Ramasamy
Statistician
CSO
Mr. Nasser Moosbally
Principal Social Security Officer
MSS
Mrs. Sweta Bhotooa
Social Security Officer
MSS
Mr. N Deerpalsing
Senior Adviser on Pension and Social
MSS
Insurance
Mrs. Sareeta Vythilingum
Higher Social Security Officer.
26
MSS