Monitoring changing lives: Dalit women in Nepal

Jane Carter, 15 January 2016
Monitoring changing lives: Dalit women in Nepal

Our sporadic series of lunchtime talks got off to a good start this year with a presentation of an Impact Assessment of the Employment Fund in Nepal, a large project managed by Helvetas and financially supported by SDC, DFID and the World Bank. We have an organisational commitment to organise two or three rigorous Impact Assessments per year – external assessments of strategically relevant projects conducted by an appropriate reputable agency. In this case, the agency was the Department of Geography at the University of Zürich, working in close collaboration with RIDA, Research Inputs and Development Action Nepal. The lunchtime talk was given by researchers Prof. Ulrike Müller- Böker and Dr Pia Hollenbach.

The Employment Fund has just reached an end, having run from 2008 to 2015; with a budget of some US $ 6-7 million per annum, and training in total over 100,000 individuals, it was amongst the largest of our projects anywhere in the world. Its strategic importance lay in having the specific aim of reaching young, disadvantaged individuals, using an incentive system. That is, the project supported training in a range of vocational skills using private training and employment service providers (T&Es in the jargon) who were paid a differentiated amount according to the social status of the trainee, and the success of their training. Social status was categorised by caste, ethnicity, wealth, and in later years, also gender, with four target groups – the most disadvantaged being economically poor low caste (Dalit) women, and others in disadvantaged circumstances such as disabled women and survivors of violence, whose participation in courses attracted the highest premium. The second most disadvantaged group were economically poor women belonging to all other castes/ethnicities. (For a bit of explanation on the complex social system in Nepal, see an earlier blog.) The success of the training was determined partly by undergoing a skills test on course completion (when the T&E received the first tranche of payment) and partly by the trained person being in gainful employment 6 months later (when the balance payment was released to the T&E). Gainful employment – and this bit is important – was defined as a monthly income of NRs 4,600 (US $ 42 at current exchange rates). The project had a detailed online monitoring and evaluation (M&E) system, designed in response to donor demand for facts and figures.

The basic question the Zürich – RIDA team set out to answer was whether this incentive system had really been effective in targeting disadvantaged women – especially since 2012, when a specific “Path to Prosperity” project component was introduced, to better meet their needs (see the example of Devika Badi as an individual who benefitted under this component). The project M&E system revealed that the programme had indeed been effective in reaching economically poor women, with the percentage increasing steadily in the latter years of project operation from around 45% to nearly 60% of all successful trainees. With regard to Dalit women, however, there was little change: they remained at around 11% of the total – the same percentage as the Dalit population of Nepal. So given that they were targeted, why no more?

It was the RIDA team who were particularly active in the field, interviewing trainees and T&E staff. One immediate observation was that Dalit women tended to opt for training in caste-based professions, as these were familiar to them. For example, women Damai (the caste of tailors/musicians), took training in garment sewing – although it should be noted this is already one step in breaking social norms, as traditionally tailors are men. This quote from a woman participating in the Path to Prosperity project component in Terhathum is illustrative:

“This trade was our culture by birth so I took this training. I took this training because I thought this can change my life.”

Many reported that training had indeed changed their life; they had become more confident, more self-assured, and with their new income they could plan a better future, as this woman from Kaski explained:

“I spend my income on household expenses. I have also planned to save some of my income. In the future, if I manage to save enough money, I will open a small garment factory. I am satisfied.”

Yet although she reported satisfaction, the woman was not recorded as a “success” in the M&E system as her monthly income did not reach NRs 4,600 after six months. Part of the reason that Dalits remain poor is that caste occupations are poorly paid; and changing mind-sets based on generations of social discrimination takes time. The measure of income “success” (kept at a universal level to minimise complications in an already complex M&E system) was set by decision-makers in Kathmandu anxious to uphold the concept of a decent wage. Possibly they were also influenced by the higher wage rates in Kathmandu compared with rural Nepal.

Trainees’ own, qualitative assessments of the results of their training are not recorded in the project M&E system.

The talk provided further justification of the arguments made in a paper discussed in an earlier blog, and echoed by our guests from the University of Zürich: figures alone do not “talk”. Without both qualitative as well as quantitative measures of project achievement, we fail to see the full picture – particularly in matters of women’s empowerment.

Jane Carter
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5 Comments for «Monitoring changing lives: Dalit women in Nepal»

  1. Sarah

    17 February 2016 at 12:07

    Thanks Jane for this excellent summary. I agree with Kai and Hedwig – the key issue is whose changes count? How to design a monitoring system that is better at not only gathering data required by project performance management frameworks but at more actively listening? There may be all kinds of changes – and changes highly valued by the people we work with – that we miss because we might not even ask about them or create/visit spaces in which they can be expressed. The question of qualitative data is a bit of a red herring, because it can also be included in databases, coded according to themes, and analysed through well established practices. It would not be so complicated to include more qualitative data in a monitoring system. In my view, the question is not about methods but about voice and accountability. How can monitoring systems become more responsive and better balance multiple voices and multiple accountabilities?

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