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The problem
Intervention: Rural-to-urban migration, with associated exposure to employment opportunities (particularly in ready-made garment industry) and urban environments
Who it was tested with: Never-married adolescent girls aged 15-19 and 20-24 years from Bangladesh; 2,700 total (1,350 urban migrants in Dhaka slums and 1,350 rural non-migrants in Matlab).
Experience from people who deliver programmes: stories, case studies and lessons learned. Valuable for understanding how and why, and for spotting what to test next, but not a measure of effect.
"The sample was selected from icddr,b's Urban Health and Demographic Surveillance System (UHDSS) and comprises of never-married adolescent girls aged 15-19, who migrated in from rural Bangladesh to slums in and around Dhaka during 2015-2019. These in-migrants were matched with their rural counterparts from icddr,b's Matlab HDSS (MHDSS), using one to one nearest neighbor matching with caliper 0.1 using propensity score matching (PSM) method." The study uses propensity score matching to create comparable groups on observed characteristics, enabling stronger causal inference than simple comparison, though unmeasured confounding remains possible.
Propensity score matching does not eliminate unmeasured confounding; the matched groups remain observational comparisons rather than randomised groups. The study captures migrants over 2015-2019 but does not follow individuals longitudinally to establish temporality. Selection into migration itself may be non-random. Findings specific to Dhaka slums and Matlab may not generalise to other contexts.
Across countries and designs
1 study · 1 country
