Labor Camp Surveys in GCC Countries: Group Quarter Subsampling

Abstract

The Gulf Cooperation Council is a regional cooperation of six Middle Eastern countries—Saudi Arabia, Kuwait, the United Arab Emirates, Qatar, Bahrain, and Oman. A common feature of these countries is the existence of many group quarters, usually called labor camps, a term used to refer to housing accommodations for unskilled migrants where nonrelated people live together. The camp size ranges from a few people to a few thousand people from many different countries who speak dozens of languages. Also, the camp size and the composition of residents inside the camps change relatively quickly as people move in and out of the camps as their labor contracts expire or project needs change. This article presents one way to subsample this dynamic population inside such labor camps. The technique was used in one survey conducted in Qatar, where more than half of the country’s population resides in labor camps.

Full text

Article
Labor Camp Surveys
in GCC Countries:
Group Quarter
Subsampling
Kien T. Le
1
, Stacy Pancratz
2
, and Abdoulaye Diop
1
Abstract
The Gulf Cooperation Council is a regional cooperation of six Middle
Eastern countries—Saudi Arabia, Kuwait, the United Arab Emirates, Qatar,
Bahrain, and Oman. A common feature of these countries is the existence
of many group quarters, usually called labor camps, a term used to refer to
housing accommodations for unskilled migrants where nonrelated people
live together. The camp size ranges from a few people to a few thousand
people from many different countries who speak dozens of languages. Also,
the camp size and the composition of residents inside the camps change
relatively quickly as people move in and out of the camps as their labor
contracts expire or project needs change. This article presents one way to
subsample this dynamic population inside such labor camps. The technique
was used in one survey conducted in Qatar, where more than half of the
country’s population resides in labor camps.
1
Social and Economic Survey Research Institute, Qatar University, Doha, Qatar
2
Pew Research Center, Washington, DC, USA
Corresponding Author:
Kien T. Le, Social and Economic Survey Research Institute, Qatar University, PO Box 2713,
Doha, Qatar.
Email: kienle@qu.edu.qa
Field Methods
2019, Vol. 31(1) 76-91
ªThe Author(s) 2018
Article reuse guidelines:
sagepub.com/journals-permissions
DOI: 10.1177/1525822X18815416
journals.sagepub.com/home/fmx

Introduction
The Gulf Cooperation Council (GCC), established in 1981, is a regional
cooperation of six Middle Eastern countries. Its member countries are Bah-
rain, Kuwait, Oman, Qatar, Saudi Arabia, and the United Arab Emirates. In
the last three decades, there has been a large influx of migrants into these
countries in response to the increase in the price of oil and the subsequent
plans of these countries for rapid development. These plans require bringing
in a very large number of foreign workers since the indigenous labor forces
are small and do not have the variety of skills required for the development
of infrastructure and other projects (Dito 2010). According to recent statis-
tics, migrants outnumber nationals in terms of the labor force in all GCC
countries. Migrants also outnumber nationals in terms of population in four
of the six countries (Baldwin-Edwards 2011).
A representative survey that studies the living and working conditions, as
well as the attitudes and opinions of the population in these countries, would
have to take into account these migrants. However, the special housing
arrangement for the migrants in these countries poses some issues about
the sample design of the survey. While some migrants live in ordinary
household units that can be sampled by the common household sample
design, many migrants live in group quarters (GQs), usually called labor
camps, which may require a different sample design.
These camps are usually provided by employers and are concentrated at
certain places away from ordinary residential household units. Inside the
camp, the number of migrants varies significantly, ranging from a few to a
few thousands, and they come from various countries and speak different
languages. Due to financial and legal reasons, these migrants cannot bring
their family with them to any country in the GCC, so there is usually no
household unit in the camp. Unrelated migrants share rooms, and people
from the same country tend to live in the same or adjacent rooms.
As migrants come to GCC countries with two- or three-year labor con-
tracts, the camp size and the composition of residents inside the camps
change relatively quickly. People move in and out of the camps every
month when their labor contracts expire or when they follow construction
projects to a new place. Thus, there is rarely an updated list of people in the
camp; instead, the sample listing inside the camp has to be conducted during
the fieldwork.
A large number of people from a variety of countries speaking different
languages dictates that the selection of persons to be interviewed inside the
camp plays an important part in the sampling process. If interviewers are
Le et al. 77

allowed to select respondents in the camp, they would select persons who
live in the same room and close to the entrance gate for convenience. More
importantly, interviewers would only select migrants who speak the same
language as his or her own language; this gives zero chance of selection for
those who speak a language different from the one spoken by the
interviewer.
In this study, we first discuss sampling methods used in previous GQ
surveys conducted in the United States and GCC countries. Based on these
existing methods and our knowledge of the camps in Qatar, we present a
method to sample migrants inside labor camps. The sampling was then used
in one labor camp survey in Qatar in 2014. We conclude the study with a
discussion of the results and implications for future studies on this topic.
GQ Sampling in Previous Surveys
Survey sampling of populations in GQ is generally designed with two or
more stages of sampling: sampling of GQ and then subsampling of residents
within a GQ. The reasons for multistage sampling are twofold. First, most
often there is not a complete sampling frame of residents in the GQ. Instead,
the project begins with only a list of GQ with perhaps some auxiliary
information such as estimated population within the GQ. The time and cost
of collecting resident-level information from each GQ to do a direct sam-
pling method would be very burdensome. Second, it is more cost and time
efficient to sample more residents within a few GQs instead of sampling
fewer residents within many GQs, due to travel and administration barriers.
Of course, higher sampling rates within a GQ increases variance estimation
levels, but proper sample design can strike a good balance between reducing
interview costs while maintaining acceptable levels of variance (Kish
1965).
The American Community Survey (ACS 2010), which has been con-
ducting GQ interviews since 2006, draws a sample of GQs each month from
a GQ-only frame. Examples of GQ included in the frame are college resi-
dence dormitories, residential treatment centers, nursing homes, military
barracks, correctional facilities, and homeless shelters. The list of quarters
is stratified by population size: small facilities of 15 residents or less and
large facilities of more than 15. GQs selected for the sample in the small
strata are selected with equal probability, and the subsampling rate within
each facility is one (interview all residents). The large stratum is list ordered
by facility type and then by geographic location. The facilities are repre-
sented once in the list for each group of 10 residents they house. For
78 Field Methods 31(1)

example, a facility with 480 residents will be listed 48 times for selection.
This method implies that more than one group of 10 can be randomly
chosen from a facility.
In the ACS, the subsampling of people inside GQ facilities is implemen-
ted in the field with two visits. First, a field representative visits the facility
and obtains the resident list from a GQ representative. Then, he or she
identifies the residents to be interviewed through a systematic sampling
procedure, using the current resident list, a predetermined interview count
(all in quarters, or 10 times the number of first-stage selections as men-
tioned above), and a computer-generated random start. An interviewer con-
ducts the second visit to the GQ to administer the survey to the preselected
residents. If a roster of residents is not available, then the field representa-
tive asks for bed locations and creates a listing from this.
In some surveys, where the GQ has a large population, interviewing a
random selection of residents can be burdensome. For this reason, an entire
cluster of respondents is usually interviewed at once. Surveys of American
schools, such as the Monitoring the Future study (http://monitoringthefu
ture.org) or the Youth Risk Behavior Surveillance System (http://www.cdc.
gov/healthyyouth/yrbs), employ a three-stage sampling design: (1) geo-
graphic area; (2) school; and (3) classroom, where the final stage of sam-
pling selects the classrooms within the schools. All students in the class are
selected for interviews. Disrupting fewer classrooms for survey administra-
tion reduces the burden on both the study team and the school’s
administration.
In a few cases, traditional subsampling is hindered when the GQ cannot
provide an accurate list of residents nor an accurate number of occupied
beds. This challenge, in some regard, is similar to the challenge of sampling
households in a large geographic range. Both situations have a finite geo-
graphic area within which the potential respondents dwell, but an accurate,
up-to-date list of elements (residents or households) is not available. The
effort to list all the elements within the area would be too excessive. Instead,
in the case of the household sampling, designs often break the geographic
area into smaller areas called segments or blocks and sample from them.
This is known as creating an area frame, where the frame is a grid of
geographic segments that cover the entire geographic area of interest. Sam-
pling segments or blocks, before listing and subsampling households within
those areas, not only eliminates the need to identify all the elements in an
area frame but also reduces the travel costs for interviewers conducting
face-to-face interviews.
Le et al. 79

In GCC countries, there are several surveys on population living inside
the camps. However, there are few publicly available documents about the
sample designs for these surveys. Through informal channels, we can only
obtain the sample design used in Qatar by the Qatar Statistical Authority
(QSA). In its 2012 Labor Force Survey, QSA used different sampling
procedures for small camps (six persons or less) and large camps (more
than six persons). The small camp sample was chosen using a two-stage
probability proportion to estimate size design, where the primary sampling
units (PSUs) were created by combining adjacent census blocks. The PSUs
had an average of 60 small camps each, and 22 camps were selected for
interview from each selected PSU. The large camp sample was chosen
using a stratified two-stage sampling process. In this case, the PSUs were
the individual camps, and they were stratified into three groups: estimated
size seven–500 residents, 501–2,500 residents, and more than 2,500 resi-
dents. The PSUs in stratum 1 (seven–500 residents) were selected with
probability proportion to size and then five persons were selected in each
camp for interview. For strata 2 and 3, all camps were selected with cer-
tainty (195 in total). For these two strata, 25 persons were sampled from
each camp in stratum 2, and 50 persons were sampled from each camp in
stratum 3. The documents from QSA did not specify the subsampling
procedures—how the camp residents were randomly selected for interview
(QSA 2012).
1
In the following, we present our sample design for a labor camp survey
conducted in Qatar in 2014. The design is based on previous designs and our
knowledge of the labor camp structure in Qatar. We especially focus on the
subsampling of respondents inside the labor camps. The large number of
people from a variety of countries speaking different languages would
obviously complicate the subsampling process. In addition, as people move
in and out of the camps frequently, the camp size and the composition of
residents in the camps change quickly, thus a roster of residents or beds
(used in the ACS subsampling) is usually not available. We will try to
address these issues in our sampling.
Sample Design
Qatar is the richest country in the GCC in terms of Gross Domestic Product
(GDP) per capita. Qatar is also the country with the highest dependence on
migrants in terms of the labor force. According to the latest census in 2010,
migrants account for more than 95%of the labor force and about 90%of the
total population. Of these migrants, about 30%live in ordinary household
80 Field Methods 31(1)

units, while the 70%live in the labor camps. This means 63%of the total
population in Qatar live in labor camps. Since the labor camp migrant
population represents such a large proportion of the country’s population,
it is essential that the sampling design for this population be precise and
unbiased in its estimates, while keeping data collection costs to a reasonable
level.
In this design, the sampling frame of labor camps, provided by the sole
water and electricity company in Qatar, is stratified by the size of camps,
and then the selection of respondents is based on two-stage process. First,
the labor camps, or PSUs, in each stratum are randomly selected with
probability proportionate to their size (PPS). The number of residents
sampled per camp is uniform within strata but varies across them—larger
clusters are selected from larger camps. The second stage of sample selec-
tion is the subsampling of people inside the camp with two visits. Each
stage is described in more detail below.
Stage 1: Labor Camp Sampling
Our sample is drawn from a frame that was developed by the Social and
Economic Survey Research Institute, with assistance from the water
and electricity company, Kahramaa, the only company providing water and
electricity services in Qatar. In this frame, all labor camps are listed with
information about the address and the number of persons living inside.
Table 1 presents the number of labor camps by municipalities in this frame.
The table shows that there is a large number of camps located in Doha, the
capital of Qatar, with a good number of ongoing construction projects
related to Qatar hosting the Fe´de´ration Internationale de Football Associa-
tion (FIFA) World Cup in 2022.
Table 1. Number of Labor Camps by Municipalities in the Frame.
Municipalities Number of Labor Camps
Doha 15,712
Al-Rayyan 6,401
Wakrah 1,815
Umm-Slala 306
Al-Khour 1,847
Al-Shamal 294
Al-Daaien 40
Total 26,415
Le et al. 81

Following the QSA sampling procedure, the frame is divided into strata
based on size, as presented in Table 2.
2
However, the size categories differ
from the QSA groupings as we opt to separate the small camps more finely
and lump more of the larger camps together in one stratum. According to
Table 2, the very small stratum with less than seven persons in each camp
accounts for 5.3%of the migrant population. Meanwhile, the very large
stratum with 200 persons or more makes up 36.5%of the migrant popula-
tion. We use proportionate allocation to ensure that these proportions in the
frame will be adhered in the sample. The benefit of stratification is to
increase the precision of statistical estimates (i.e., a decrease in the standard
error); the larger the difference between strata on demographic character-
istics and variables of interest, the larger the increase in precision. It is
expected that the characteristics of people are likely to vary based on camp
size. People in larger camps usually have lower income and lower education
than those in smaller camps. This expectation will be verified later in the
Survey Results section.
The last column of Table 2 shows the number of persons to be selected in
each camp for different strata. We selected one person for the very small
type, two persons for the small stratum, and so on. The decision to sample
more persons in larger camps is based on the expectation that larger camps
have more variation (or lower correlation) within their population, as
opposed to smaller camps. The additional interviews should capture the
increased level of variation. We will show the variation across strata in
some key variables in the Result section.
Having stratified the frame, the camps within each stratum can be
selected with PPS.
3
Considering that there are fixed numbers of people to
be selected in each stratum, the PPS method helps equalize the chance of
selection of labor migrants in each stratum as well as in the whole sample
Table 2. Number of Camps and Persons by Strata.
Strata
Number of
Labor Camps
Proportion of
Persons (%)
No. of Persons
Selected in Each Camp
Very small (less than 7) 10,398 5.3 1
Small (7 to less than 20) 8,533 14.2 2
Medium (20 to less than 50) 4,882 20.0 4
Large (50 to less than 200) 1,987 23.9 8
Very large (200 or more) 530 36.5 16
Total 26,330 100
82 Field Methods 31(1)

due to the proportionate allocation across strata. In other words, the data are
self-weighted, and there is no need to calculate the sampling weights.
However, the camp size changes so quickly that the actual camp size col-
lected during the fieldwork sometimes differs from the one in the frame. For
example, a camp, which is considered small in the frame, selected through
the PPS method can be found to have significantly increased in size by the
time of data collection. For this camp, a sampling weight is required to
offset the increasing camp size. The opposite problem occurs for very large
camps that are found to have shrunk in size. Therefore, sampling weights
are needed to account for the changing camp size.
4
Stage 2: Subsampling Inside the Camps
As mentioned above, the subsampling of people inside the camp is an
important part in the sampling process due to the large number of people
from various countries speaking different languages. In the following, we
describe the sampling method in general, followed by the specific steps
used in the field.
Sampling method. In the ACS (2010), the subsampling inside GQ is made
easier by the list of resident names living inside the quarters. However, in
our labor camp survey, this list is usually not available. Furthermore, the
number of residents inside the camp changes quickly, preventing the camp
from tracking which and how many beds are occupied on any particular
day. To tackle these issues, we take inspiration from the sampling proce-
dures of the American school surveys and area-based frames. Instead of
conducting a full listing of the camp population with potentially thousands
of residents, we introduce an intermediary sampling stage—the room. The
following describes the selection of the room and then bed numbers inside
the camp.
First, the selection of rooms is conducted with circular systematic sam-
pling. Systematic sampling procedure stipulates that rooms are chosen by
taking every kth room in the camp, where kis called the sampling step (the
ratio between the number of rooms in the camp and the number of rooms to
be selected, rounded to nearest whole number). For instance, if there are 13
rooms in a camp and four rooms need to be selected, then the sampling step
to be used is the whole number part of 13/4, which is three. Next, a random
number from one to 13 is generated, say number five. The selected room
numbers are five, eight, 11, and one. As labor migrants from the same
country tend to live in adjacent rooms, the selection of rooms by systematic
Le et al. 83

sampling helps reduce the chance of selecting people from one country,
hence increasing the variation in sampled people’s characteristics.
This step mimics the area-based sampling method of creating segments
or blocks. Rooms are permanent, clear divides of the camp population, and
rooms are assumed to house approximately the same number of migrants
within a camp. Plus, each migrant is assigned one and only one room. This
is similar to the aim of drawing area segments with recognizable, permanent
boundaries and with approximately the same number of households in each.
Second, one person in each room is randomly selected by his bed num-
ber. For example, if there are 10 occupied beds in the room (do not include
empty beds in list), the computer will randomly select one number from 1 to
10, say 4. Then, the person in bed number four is selected for the survey. An
alternate way to select a person in the room is to ask for the name of
everyone in the room. However, this method is very time consuming as
some rooms can have dozens of people inside.
Sampling in the field. The sampling method described above is based on
information on the number and location of rooms as well as the bed number.
However, this information is not available in the frame, so the selection of
rooms and the person inside the room has to be done during the fieldwork in
two visits as follows.
First, a supervisor (with a computer) is sent to the selected camp. On
arrival, he asks for the number of occupied rooms in the camp. Then, the
computer (using systematic sampling) shows the room numbers to be
selected. Since there are not usually room numbers in the camp, the super-
visor is instructed to count rooms from left to right, starting from the room
closest to the main entrance gate. Having selected the rooms, the supervisor
asks for the number of occupied beds in the selected rooms, and the com-
puter randomly selects a number from one to the number of the beds. Like
room numbers, there are no bed numbers in the rooms, so supervisors count
the beds from left to right and select the bed with the number generated by
the computer. Next, the supervisor asks for the name and language spoken
by the person of the selected bed.
5
Note that he can do this with anyone who
is available in the room, not necessarily with the selected person. The
supervisor then leaves the camp without interviewing the selected person.
Before leaving, he puts a sticker on the doors of selected rooms.
Second, interviewers with the appropriate language skills are assigned to
visit the camp to conduct the interviews with the selected persons in the
camp. The interviewers locate the selected rooms in the camp with the
stickers and then conduct the interview with the selected person in the room.
84 Field Methods 31(1)

The main reason for the two visits to the camp (one by the supervisor and
one by the interviewer) is to resolve the language issue. Without informa-
tion about the language of the selected persons, we would not be able to
send the right interviewer(s)—interviewer(s) with the proper language
skills to conduct the interview(s)—to the camp. The quality of the data
could be hampered if interviewers and respondents do not fully understand
each other due to language differences. Another reason for the two visits is
about the gatekeeper issue. Having a supervisor who is better trained and
more experienced is sometimes necessary to gain access to the camps.
Overall, the two visits increase the field cost but are needed to ensure the
survey quality.
Survey Results
The 2014 Omnibus Survey of Qatar implemented the sampling method
described in this article to select a sample of migrants living in labor camps.
Table 3 shows the number of camps and respondents interviewed in each
stratum. A total of 645 respondents from 133 labor camps were interviewed.
The last column shows the proportion of respondents across strata. Approx-
imately, one-fifth of the population lives in a very small or small camp;
another one-fifth lives in a medium-sized camp; one-quarter lives in a large
camp; and over one-third of the population lives in a very large camp. These
proportions are similar to those in the frame (see Table 2), as a result of the
proportionate allocation to strata.
Camp Size: Frame Information and Field Observation
The sampling frame provided by Kahramaa includes estimates of the num-
ber of migrant workers living in each labor camp. The estimates often do
not match the numbers reported by the camp representatives when the field
Table 3. Distribution of Camps and Respondents across Strata.
Camp Types Camps Respondents Proportion (%)
Very small (2–6 residents) 27 27 5.3
Small (7–19 residents) 46 92 14.4
Medium (20–49 residents) 26 120 20.2
Large (50–199 residents) 19 153 24.2
Very large (200þresidents) 15 253 35.9
Total 133 645 100
Le et al. 85

supervisor conducts the first visit to the camps. Camp sizes change rapidly
due to current project needs and workers constantly arriving from and
leaving for their home countries. This results in some camps being mis-
categorized in the strata. In some instances, a camp that had been placed in
the stratum for small-sized camps (seven–15 residents), based on initial size
information from the sample frame, may actually have more than 15 resi-
dents when the field supervisor first visits the camp for the survey. This
means the camp should have been placed in a different stratum if informa-
tion about the true camp size was known during sample design. Table 4
shows how often this situation occurred in the 2014 Omnibus Survey field-
work. The numbers in the diagonal show the number of camps with no
change from the frame to the field, while the numbers off the diagonal show
the difference between the frame and the field. For example, 17 camps were
placed in the stratum for very small camps; 11 of the 17 camps were found
to actually have two to six residents. However, five labor camps were found
to have increased in size to have between seven and 19 residents, and one
camp had increased to have 20–49 residents. For each stratum, we do
observe a large proportion of camps increasing or decreasing their numbers
to the extent that they are changing their camp size stratum.
Migrant Worker Demographic Characteristics by Strata
The extent to which camps are miscategorized into strata, as demonstrated
by the previous table, leads us to question whether the stratification process
is still worthwhile. Note that the main goal of stratification is to increase
precision of the estimate, and this goal can only be achieved if there is
significant difference in population characteristics across strata. Table 5
provides evidence that the respondents in each strata are significantly dif-
ferent from each other on several demographic characteristics.
Table 4. Camp Size Change from One Stratum to Another Stratum.
Measure of Size in Frame
Actual Measure of Size (from the Field)
Very Small Small Medium Large Very Large
Very small (2–6 residents) 11 5 1 0 0
Small (7–19 residents) 5 18 7 3 2
Medium (20–49 residents) 2 4 9 9 1
Large (50–199 residents) 0 2 5 7 4
Very large (200þresidents) 0 0 0 3 12
86 Field Methods 31(1)

Respondents in strata 1 and 2 (very small and small camps, as estimated
in the frame) are generally older, by four to five years, than respondents in
other strata. The respondents in stratum 1 are more likely to have completed
some postsecondary education, compared to others in strata 2–5. In general,
respondents’ level of income decreases from stratum 1 to stratum 5. Marriage
rates of respondents differed across strata but not in a linear pattern like the
other characteristics. We use analysis of variance to test for differences across
strata. The pvalues of the tests are presented in the last row. Marital status is
statistically significant at the 5%level, while other demographics (age, edu-
cation, and income) are all significant at 1%level. These data suggest that the
flawed stratification is still useful in the sampling process.
Respondent Nationality
Table 6 displays the tabulations of respondents’ home countries. One-
third (33%) of the sample is from Nepal. India is the second most
common home country (28.5%). Approximately, one in six labor
migrants (15.5%) is from Bangladesh. Other common home countries
areSriLanka(5.9%), Egypt (4.5%), Pakistan (4.1%), and the Philip-
pines (3.0%). The variety of nationalities shows the importance of
matching interviewer’s language to respondent’s language. This justifies
the use of two visits during the fieldwork whereby the respondent’s
language is identified in the first visit, and the interviewer with the
right language can be selected for the second visit.
Table 5. Demographic Differences across Strata.
Strata Mean Age
Completed
Postsecondary
Education Married
Monthly Income
(Qatar Riyals)
Very small (2–6 residents) 36.6 .466 .745 3,339
Small (7–19 residents) 37.7 .139 .693 2,291
Medium (20–49 residents) 32.6 .150 .647 1,808
Large (50–199 residents) 32.5 .039 .713 1,310
Very large (200þresidents) 33.2 .078 .762 1,390
Total 33.8 .113 .72 1,688
ANOVA (pvalues) .00 .00 .05 .00
Note: ANOVA ¼analysis of variance.
Le et al. 87

Camp and Respondent Response Rates by Strata
Camps had an overall response rate of 83%, and once inside a cooperating
camp, selected residents responded overall at a rate of 97%. Overall and
stratum-specific response rates are reported in Table 7. The response rates
for camps were highest for very small and small camps (93%and 92%,
respectively), while the medium, large, and very large camps responded at a
lower rate of 72%,76%, and 75%, respectively. Respondent response rates
were very high for all groups, with the lowest response rate of 94%, from
the very large camp.
Intra-camp Correlation
In our sample design, the number of selected persons in each camp varies
across strata. For example, in the very small stratum, only one person is
Table 6. Respondent Distribution by Nationalities.
Country %
Nepal 33.4
India 28.5
Bangladesh 15.5
Sri Lanka 5.9
Egypt 4.5
Pakistan 4.1
Philippines 3.0
Other 5.2
Total 100
Table 7. Response Rates by Strata.
Strata
Camp Survey
Response Rate (%)
Interview
Response Rate (%)
Very small (2–6 residents) 93 100
Small (7–19 residents) 92 100
Medium (20–49 residents) 72 99
Large (50–199 residents) 76 100
Very large (200þresidents) 75 94
Overall 83 97
88 Field Methods 31(1)

selected from each camp, while 16 are chosen in the very large stratum.
The justification for this difference is based on our expectation that there
is more variation in the big camps than the small camps. To check this
expectation, we look at the intra-cluster correlation (ICC) coefficients
across strata for some demographics and key variables of interest
(see Table 8).
For age of respondent, the ICC for each stratum is relatively small (r¼
.08, .25, .03, .07). For level of education of respondents, the ICC decreased
in value from stratum 2 to stratum 5. The level of variation in education
level within a camp is greater in large camps than in small, which is what
the design team assumed when previously determining subsampling rates.
The ICC for respondents’ level of income followed a similar pattern to age,
in that the ICC for each stratum was generally low (r¼.17, .28, .10, .17),
while the overall ICC was higher at 0.47. Thus, demographic variables tend
to follow one of two patterns: Either the ICC is high among smaller labor
camps and decreases with labor camp size (in the case of education leve1);
or the ICC is relatively stable within any strata, but intra-strata correlation is
evident (in the cases of age and income). Table 8 includes ICC values for
two variables of substantive interest. Job satisfaction and work treatment
satisfaction (both a scale of 1–5 with 5 ¼“very satisfied”) followed the first
pattern described above. The ICC was higher in the smaller camps, and the
coefficients decrease in value as the camp size grows.
Discussion
Migrant workers residing in labor camps represent a large and growing
segment of the population in Qatar. In light of the criticism about the
living and working conditions for the 2022 World Cup construction
Table 8. Intra-cluster Correlation Coefficients.
Camp Types Age
Education
Level
Monthly
Income
Job
Satisfaction
Work
Treatment
Small (7–19 residents) .08 .55 .17 .44 .49
Medium (20–49 residents) .25 .49 .28 .39 .36
Large (50–199 residents) .03 .28 .10 .17 .43
Very large (200þresidents) .07 .18 .17 .19 .22
Overall (excluding very small
camps)
.25 .47 .42 .21 .34
Le et al. 89

workers, the demand to study and understand the population has also
grown. It is a unique population to sample and interview because they
are very diverse ethnically and linguistically as well as rapidly changing in
size and location.
This article presents one way to sample migrant laborers using a strati-
fied two-stage selection approach. The camps, or PSUs, are proportionately
stratified by camp size, then the selection of camps is conducted with the
probability proportion to size method. Subsampling selection is conducted
in the field by a supervisor since updated lists of camp residents often do not
exist. The supervisor systematically selects room(s) in the camp and then
systematically selects one occupied bed in the room(s). An interviewer with
the needed language skills visits the camp at another time to conduct the
interview with the chosen respondent.
Stratification is valuable, although camps may drastically change in size
between the time the size is recorded in the frame and when the field team
conducts the interviews. However, the changes do create problems else-
where in the sampling process. Foremost of these is that sampling weights
are negatively impacted. A camp, believed to be small, selected through the
PPS method and then found to have doubled or tripled in size by the time of
data collection will yield an extremely large sampling weight; the opposite
problem occurs for very large camps that are found to have rapidly shrunk
in size. The extremely high and extremely low weight values inflate the
survey’s variance estimates. Although weight trimming helps mitigate this
problem,
6
more work needs to be done on how to obtain more accurate
camp sizes for the frame or find methods to mitigate the effects of the rapid
changes.
The analysis of ICC overall and within each stratum revealed two rela-
tionship patterns. First, just as the design team had estimated, the ICC
values are highest in the very small camp strata, and the values gradually
decrease as the strata’s average camp size increases. When the ICC values
change across strata, an efficient sample design will vary the number of
elements selected in each cluster, just as the design does now.
Most subsampling procedures require a full listing of elements. The
proposed sample design approach takes methods from multiple-stage sam-
pling designs and area listing frames to subsample within labor camps
lacking up-to-date resident lists. Choosing rooms in a camp with a sys-
tematic random procedure overcomes the problem while maintaining the
ability to select a subsample as diverse as a simple random subsample.
We hope that this proposed sampling design and its criticisms previously
mentioned will add to the discussion of unique challenges in sampling
90 Field Methods 31(1)

diverse and dynamic populations in GQs such as the migrant workers
residing in labor camps in Qatar or in other GCC countries.
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect to the research,
authorship, and/or publication of this article.
Funding
The author(s) received no financial support for the research, authorship, and/or
publication of this article.
Notes
1. We have talked to some interviewers who did the Labor Force Surveys, and they
said that they were told to randomly select people in the camp but that it is up to
them to decide who these people are.
2. According to the Qatar Statistical Authority classification, the very small type is
called small collective households (with less than seven persons), while the rest
are called large collective households (with seven or more persons).
3. One camp with the biggest size in stratum 5 was chosen with certainty.
4. Due to the word limit for this article, we are unable to present details of the
weight calculation. However, this calculation can be provided on request.
5. In some camps, the number of rooms is less than the number of people to be
sampled. In this case, the supervisor can select two persons in the room.
6. We trimmed the weights at the top 5%and bottom 5%of the weight distribution.
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