Example from a survey in Burundi
Here we give a couple of examples of the standard outputs we generate using the RHoMIS survey and the indicator calculation and visualization software. The examples are certainly not extensive, and we capture and quantify many more indicators, but they do give you a flavour of the possibilities we have with RHoMIS, and the type of results we can produce within one week after a survey is completed.
The data used for the analysis presented below was collected in Burundi in early 2018 in a project funded by IFAD and the European Union. |
RHoMIS data analysis is rapid: first results are available within one week of survey completion. |
Besides these welfare indicators we also capture revenue and income information, which we use to quantify the monetary value of the different activities that households have. We quantify this information for each individual household (Fig. 7), but also define so-called poverty quantiles (the 25% of households scoring the ‘lowest’ in the total value of their activities, up to the households scoring the 'highest' values). The quantiles allow us to analyse in more detail the behaviour and choices of households in the individual wealth brackets (Fig. 8).
We typically see systematic differences between these household groups, with the poorest mostly depending on crop activities (consumption and sales), whereas for the highest quartile livestock and off farm income generate most income (Fig. 8).
With this wealth grouping we can then investigate how other characteristics differ between wealth groups. An example given here is our gender equity indicator (Fig. 9) which quantifies the control that women have over the benefits of on- and off-farm activities (e.g. food and cash). We see that for the poorest households the gender equity score is good (women have almost 50% control over income and food), but for wealthier households we see that men have more control over the sales of agricultural produce, resulting in a lower female control score, indicating gender inequity. Underlying these overall gender equity scores are the female control scores for individual activities and agricultural commodities which we use to identify possible intensification options that do not lead to unbalances in gender equity.
We typically see systematic differences between these household groups, with the poorest mostly depending on crop activities (consumption and sales), whereas for the highest quartile livestock and off farm income generate most income (Fig. 8).
With this wealth grouping we can then investigate how other characteristics differ between wealth groups. An example given here is our gender equity indicator (Fig. 9) which quantifies the control that women have over the benefits of on- and off-farm activities (e.g. food and cash). We see that for the poorest households the gender equity score is good (women have almost 50% control over income and food), but for wealthier households we see that men have more control over the sales of agricultural produce, resulting in a lower female control score, indicating gender inequity. Underlying these overall gender equity scores are the female control scores for individual activities and agricultural commodities which we use to identify possible intensification options that do not lead to unbalances in gender equity.