Anti Child Trafficking

Udaipur, India

Conducting the Situation Analysis report for the project “Combating child trafficking through sustainable livelihood development”, in Udaipur, Rajasthan

In the context of the project “Combating child trafficking through sustainable livelihood development in Udaipur, Rajasthan” SOCEO was assigned to conduct a situation analysis study in 2016.

The project itself aims to empower the socially and economically backward population of two sub-districts of Udaipur in Rajasthan in the western part of India. School enrolment is promoted and awareness-raising actions are carried out. Thereby, the vulnerability of the inhabitants of the 15 target villages is reduced and the risk of becoming a victim of child trafficking will decrease. A study of ChildFund India in 2008 revealed that more than 10.000 children were trafficked from Udaipur district into Gujarat. There, they work in BT cotton fields (genetically modified cotton varieties) and have to live under inhuman conditions.

The foundation to empower the participants economically is SLDP (Sustainable Livelihood Development Programme) about whose implementation in Udaipur you will get to know more here

The Situation Analysis

In line with project design, in order to understand the socio economic factors leading to child trafficking and mapping of existing value and supply chains of rural economy in the two districts of Udaipur, SOCEO undertook the assignment of a “situation analysis” study.

For a qualitative survey the SOCEO research team visited the project villages and had focused group discussions with farmers, artisans shopkeepers, and NGO workers.

Furthermore, primary quantitative data about the annual income, household size, average land holding, child education and status of children getting trafficked or working as child labour/school drop -out, livelihood pattern and different source livelihood, surplus crop, community based infrastructure etc. was collected by the SOCEO research team.

As sampling methodology, the study adopted a multi stage cluster-random sampling technique. In the first stage, 15 target villages were grouped into 6 clusters. From each cluster, one village was randomly selected, which resulted in six randomly selected villages. In the next stage, households were selected randomly from each selected village. The number of households from each village was proportional to the size of that particular village (in terms of number of total households living in the village). In the randomly selected villages 300 households were selected randomly, with the sample of 300 households representing about 10 % of the target population.

To gain in addition a general overview of the area, the geography, demography, economy, geo-climatic and other factors contributing to poverty, existing and possible value chains, economic opportunities, etc., the study team engaged in revisiting the census data and other relevant literature.