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UID:pretalx-2024-8LYFSB@conferences.acspri.org.au
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DESCRIPTION:Background\nEstimates of forced labour in the Arab States regio
 n are conservative due to difficulties in conducting population-based surv
 eys of this target group in this region. Forced labour in the Arab States 
 is expected to be more prevalent in sectors which rely heavily on migrant 
 workers\, many of whom do not speak Arabic and reside in locations that ar
 e not accessible such as dormitories or employers’ homes. In 2019\, the I
 nternational Labour Organisation estimated the Arab States hosted 24.1 mil
 lion migrant workers\, 14% of the global figure. This study sought to over
 come barriers to conducting surveys on forced labour in the Arab States us
 ing a novel approach to sample those who had recently worked in the Arab S
 tates and since returned to one of six major sending countries. \nAim\nThe
  primary aim of this study was to estimate prevalence of forced labour amo
 ng migrant workers in the Arab States. \nMethodology\nTo achieve the targe
 t sample size of 2\,000 respondents in each survey country\, a hybrid desi
 gn was used\, combining a multi-stage probability design (nationally repre
 sentative household surveys) with respondent driven sampling (RDS). The ta
 rget population was persons aged 15 years and older who had worked in an A
 rab State in the preceding five years\, regardless of their type of work (
 returned migrant workers). The surveys were conducted face-to-face in six 
 countries known to be major source countries for migrants to the Arab Stat
 es\, namely\; India\, Bangladesh\, Pakistan\, Indonesia\, Philippines\, an
 d Ethiopia. \nThe sampling design aimed to recruit 50 percent of the sampl
 e via multi-stage household sampling and 50 percent via RDS to balance the
  increased efficiency from the RDS sampling with the well-established prob
 ability design. RDS “seeds” who referred other members of the target pop
 ulation were identified through the household sampling and by relevant loc
 al organisations.\nProbability weights were calculated for the household s
 urvey sample and calibrated to population benchmarks from the most recent 
 national census. Statistical matching via a nearest neighbours’ method wa
 s used to determine appropriate weights for each record in the nonprobabil
 ity sample by imputing weights from the matched records in the probability
  sample. \nResults\nA total of 10\,302 returned migrant workers were surve
 yed\, with 48.5 percent recruited via RDS and 51.5 percent recruited via h
 ousehold sampling. Of the total returned migrant workers\, 18.5 percent ha
 d been in forced labour in an Arab States country in the preceding five ye
 ars. There was no significant difference in occurrence of forced labour be
 tween those recruited by RDS and household sampling. Issues encountered du
 ring the collection of the RDS combined with the experimental nature of th
 e approach to weighting the RDS sample limited our ability to produce reli
 able estimates of the prevalence of forced labour among returned migrant w
 orkers from the Arab States. \nConclusion\nA hybrid design that combines p
 robability and nonprobability sampling approaches offers the potential to 
 recruit a sample from which reliable estimates can be drawn. In this study
 \, the use of random household sampling and respondent-driven sampling pro
 vided insights into the strengths and challenges of this approach. We pres
 ent a summary of these findings to inform future research with similar obj
 ectives.
DTSTAMP:20260716T073234Z
LOCATION:Cullen Room
SUMMARY:Forced labour of migrant workers in the Arab States: A novel approa
 ch to sampling a hard-to-reach population - Elly Williams
URL:https://conferences.acspri.org.au/2024/talk/8LYFSB/
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