Results from a before-and-after learning assessment of pre-migration departure training for prospective female migrants in Odisha, India: Data Guide

Citation

The following citation is recommended:

Pocock, NS and Kiss, L (2020). Results from a before-and-after learning assessment of pre-migration departure training for prospective female migrants in Odisha, India. [Dataset]. London School of Hygiene & Tropical Medicine, London, United Kingdom. https://doi.org/10.17037/DATA.00001789

Description

Work in Freedom (WiF) was a large multi-component intervention managed by the International Labour Organization (ILO) and funded by the Department for International Development (DFID), to prevent labour trafficking. WiF was implemented in India, Nepal and Bangladesh as source countries, and Lebanon and Jordan as destination countries, from 2013 to 2018. WiF’s community-based activities aimed to prevent labour trafficking by enhancing women’s autonomy, fostering adoption of ‘safe migration’ practices and assertion of migrant workers’ rights.

The India component of WiF focussed on prospective in-country migrants only. A two-day pre-migration training event was organised for women who expressed an interest in migrating for work during WIF direct outreach sessions. The purpose of the sessions was to help women to make informed decisions about whether, and how, to migrate for work. They included modules on self-care, financial literacy, use of technology to ensure wellbeing, and safety in transit and at destination. Sessions were organised at the Gram Panchayat (village assembly) level and held once approximately 30 women had signed up per session (multiple sessions were held with different groups of approximately 30 women). The training was implemented by the Self Employed Women’s Association (SEWA) and AAINA, local NGOs with experience in gender, and labour mobilisation and organisation among women.

This dataset contains data provided by 347 women who participated in pre-migration training and completed pre-training and post-training interviews. Dataset variables are listed in the accompanying questionnaire.

Dataset access process:

This dataset is restricted to protect participant confidentiality. However, researchers interested in the dataset are invited to contact the study team to access it. The application process is outlined below.

1. Review questionnaire

Review the questionnaire made available alongside the dataset (S1_Structured_Survey_Instrument.pdf) and make a note of the question number (e.g. 2.5, 3.1) of responses that you wish to access.

2. Complete the web form

Click the Dataset ‘REQUEST ACCESS’ button and complete the web form. Alternatively, you can contact the data creators directly via email – see the accompanying paper for contact details.

The request web form will ask you to provide the following information:

  1. Intended use: Outline how you propose to use the data (e.g. in a funded study, student summer project) and the type of analysis to be applied
  2. Data requested: Indicate the data that is being requested – responses to specific questions or full dataset.
  3. Evidence of relevant experience: Provide any additional information that may support your access request, e.g. academic profile
  4. Name of the person who will be using the data or leading the analysis process
  5. Contact email address
  6. Institution or organisation affiliation (if any)

*Don’t forget to press the ‘REQUEST ACCESS’ button at the bottom of the page to submit your request*. Your request will be sent to the data creators and the repository manager.

The request will be reviewed by the data creators, who may contact you to clarify details. Contact researchdatamanagement@lshtm.ac.uk if you do not receive a response within 14 days of your request.

3. Sign a licence agreement

If the data creators possess the requested data fields and are able to provide them, they may ask you to sign a licence agreement prior to being provided with a dataset tailored to your request.

Anonymisation procedures

Identifiers that have been removed completely from the dataset include:

Raw data on the following potentially identifying variables have been recoded in this dataset:

The following variable has been deleted: