Methodology for Delhi Urban Profiling: Household survey: Sampling Strategy
JIPS/Tufts, March-April 2013
Introduction
Our goal for the profiling survey was to compare the experience of UNHCR’s population of concern –
Burmese, Afghan1 and Somali refugees – with their neighboring Indian nationals. This refugee population
amounts to less than 0.08 percent of the total population living in the National Capital Territory of Delhi
(around 23 million people), and identifying UNHCR ‘s population of concern was a challenge. The refugee
communities tend to be spread across different parts of Delhi. Some refugee groups, such as the Somalis,
include households, who use residential mobility in order to cope with difficult financial situations and
discrimination.
Given the (i) heterogeneity of the patterns of settlement between the three target groups (see below:
mapping) and (ii) the wide range of distribution within the refugee population (from 200 Somali individuals
to 6,000 Chins), a simple random sampling would not yield enough refugee respondents in each of our
targeted refugee groups. Moreover, a single sampling strategy was not appropriate for all the groups, so we
devised three sampling approaches, each one tailored to each of the three surveyed populations.
Sample size
Our initial target sample was 1,200 households, in four districts and one municipality of the National Capital
Region of Delhi, of which about a third would be Indian neighbors living near refugee households. UNHCR’s
refugee registration database, called Progres, was an important source of information. It provided details
for each registered household on ethnicity, country of origin, religion, address in Delhi, etc.
A change in the originally calculated sample had to take place for the Afghan community. During the data
collection, many sites listed in the Progress were found not be inhabited by Afghan refugees any longer.
After using additional sources of information, such as the list of those Afghans recently granted the new
UNHCR cards (called Blue Card or Smart card) and the beneficiaries’ lists from Don Bosco, UNHCR’s
implementing partner, we managed to interview 200 households. The sample therefore had to be reduced
accordingly from the originally planned 400 to 200. Subsequently the overall sample number was reduced
to a total of 1,000 interviews, as detailed below (chart 1).
Chart 1: Expected Distribution of HH interviews
Indian, 350
Afghanistan,
200
Myanmar,
400
Somalia, 50
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Based on discussions with UNHCR, we decided to interview only ethnic Afghans, who had come to Delhi in the past
5 years, and not Hindu-Sikh Afghans who had come to Delhi more than twenty years earlier and were regarded as well
integrated into Delhi.
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Identifying and mapping target groups localities
The very low density of the target population at metropolitan level required the use of secondary
information (UNHCR’s Proges database, key informants, etc.) to identify the main areas of settlement of the
refugee population. We began by sorting the Progres data according to refugee group and locality, and
eliminating localities with less than 10 households. This breakdown enabled us to identify the main
clustering of refugee population in Delhi, as shown in Charts 2 and 3.
Chart 2: Total refugees – (localities with less than 10 households removed)
1600
1400
1200
1000
800
600
400
200
0
Chart 3: Refugee’s breakdown analysis
Country of Origin: Myanmar
30.00%
25.00%
20.00%
15.00%
10.00%
5.00%
0.00%
As with other urban profiling exercises, we stratified the identified administrative areas according to high,
medium and low densities of refugee population. We then plotted out on Google maps the (i) locations, (ii)
estimation of the UNHCR’s population of concern and (iii) densities. We focused on the high-density areas
(see map 2), such as the ward Vikash Puri and Janakpuri.
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Maps 1&2: Location and densities of urban refugees in West Delhi
The pattern of refugee distribution in Delhi is complex, in that each refugee community has different
settlement strategies. The Burmese Chin refugees are mainly clustered in three wards in South Delhi
(Bodella, Janakpuri/VikashPuri and Hastal Village) where they comprise about 2-5 percent of the
population. Their pattern of settlement is somewhat different from other cities, in that the Chin refugees
tend to live together in apartment buildings. The Afghan population is more spread out in four districts of
low population density (Malvya Nagar, Lajpat Nagar, Wazirabad). The Somalis comprise a very small
population (around 200 individuals) and required individual identification.
For each target group, we used a different sampling strategy as explained in the next section.
The next stage was to identify the sample sites within the identified wards for each of the three target
groups. We began with the Progres listed addresses, but these were not properly reported or were out of
date (see next step below), so we worked with community leaders and UNHCR’s implementing partner
(Don Bosco) ‘animators’ to find out where refugees were living in each ward. The team identified different
landmarks around which refugees were expected to be living, and conducted scoping field visits to verify
the actual presence of refugees and refine the mapping. By pre-identifying blocks, where refugees were
living, we facilitated subsequent refugee household identification during the enumerators’ deployment.
At each pre-identified sample site, the enumerators interviewed three refugee households and then
randomly selected Indian households, who were neighbors of the refugees. The results enable comparisons
between Indian Delhi natives, Indian migrants, foreigner/non-refugees and refugee groups.
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Sampling strategies for each target group
A. PPS approach (Myanmar (Burmese Chin) community)
The statistical unit for this survey is the block- identified through landmarks- (hereafter Sample Sites).
In Step 1, we used the progress database and a population proportional to size (PPS) strategy to randomly
select 20 Enumeration areas out of the 30 Sample Sites. PPS is useful when sampling units vary in size
because it assures that households in denser EAs have the same probability of getting into the sample as
those in smaller sites, and vice verse. This method also facilitates planning for fieldwork because a predetermined number of respondents is interviewed in each unit selected, and staff can be allocated
accordingly.
Steps in Applying PPS Sampling
1. List all sample sites, within the already categorized high density areas; the number of estimated households within
each; and the running cumulative (hereafter runcum- population).
2. We want to select 20 EAs. Divide the total no. households by 20; the result is called the Sampling Interval (SI).
3. Choose a random number between 1 and the SI. This is the Random Start (RS).
4. Calculate the following series: RS; RS + SI; RS + 2SI ….
5. Each of the 20 numbers corresponds to an EA site in the list of Sample Sites. The EA selected are those for which the
cumulative population contains the numbers in the series we calculated. Some of the Samples Sites (specifically three)
have been selected twice (e.g. Western Union bank site) since under its numbers RUNCUM contains two series).
Table 1: Drawing a Sample Using Probability Proportional to Size Sampling in Chin’s localities
Estimated
Localities
Sample Sites
HH
RUNCUMU
RS+SI
Monday Bazar
30
30
25
Boby Photo Shop
80
110
73,4
Western Union Bank
50
160
121,8
Chanakya
Place
40 fts Road
80
240
170,2
Jalemdar Car Workshop
11
251
218,6
Sunday Bazar
40
291
267
Anaan Shop
30
321
315,4
Asaltpur
Car workshops
20
341
363,8
Matajanan Davi hospital
20
361
412,2
Sevenday Adventist School
50
411
460,6
Near
DMCF
church
50
461
509
Sitapuri
Near Dabri Police station/ DBA creche
center
50
511
557,4
Bank of India
10
521
605,8
Bodella Market
30
551
654,2
Bosco Delhi
30
581
702,6
Bodella
BCRC Hall
50
631
751
White house
30
661
799,4
Near DC II, III
15
676
847,8
4
Hastal
Om Vihar
Old Janapuri
LIG Hastal
LIG, BAG church
LIG community hall
People Chawk 1 &2
Hastal Qutab Wala
Subh Nursing Home
Janta Flat
Party loan
2CD church
MCD School
Budh Bazar
Matachanchan Hospital
Bindapur/Old Janapuri
40
15
15
25
35
12
5
15
35
25
30
10
30
716
731
746
771
806
818
823
838
873
898
928
938
968
896,2
944,6
993
In Step 2, during the pre-identification stage, we randomly selected 20 households in each of the 20 Sample
Site selected in Step 1, for a total sample of 400. This selection occurs by randomly selecting 6-7 apartment
buildings in each EA, then going to these locations and randomly selecting the nearest 3-4 Burmese
dwellings plus 2-3 Indian/non-refugee dwelling for interviews.
B. Random identification and snowball methods (Afghan community)
The sampling strategy for the Afghan communities had to address the very low densities of this target
population. We used UNHCR’s Progres database to randomly identify addresses, selected with a random
sequence generator (www.random.org/sequence). If no Afghan nationals were living in the selected
apartment building (many lists and address were outdated), the enumerators selected up two
replacements from neighboring blocks of buildings.
Once an Afghan household was identified, enumerators used a ‘snowball’ technique (often used in hidden
populations which are difficult to access) to complete the desired sample. In order to limit/prevent bias we
only selected up to four households through snowballing. After that, enumerators had to move to the next
randomly identified address and proceed again with the interview and snowballing.
C. Enumeration (Somali community)
In view of the very limited number of Somali refugees living in Delhi (around
210 individuals), we sought to survey all of them. We identified their
addresses using the Progres list, the IP’s beneficiaries’ lists and field visits with
key informants (see photo, Somali enumerators and key informant, in
Wazirabad, North Delhi).
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