In l-gorman/rhomis-R-package: Essential Processes for RHoMIS Data

Indicators Explained

The core RHoMIS survey contains over 750 variables. These are used to calculate key indicators, such as total_income_lcu_per_year.

This guide is designed to explain:

How each indicator is calculated
How variables are linked
How you can explore these calculations and indicators yourself

Main Principle in Calculations

RHoMIS is designed to collect information on wide range of topics in a short amount of time. Questions in the survey are asked in a way that we can make estimates, whilst not forcing the respondent to provide a level of precision they are not able to.

To calculate indicators such as income, productivity, gendered control of resources, we rely on proportions and recall. This is best explained using worked examples (see below)

Worked Example

When calculating crop productivity, respondents are asked to give the amount harvested over the past 12 months, they are then asked to give the unit of this amount (e.g. 'kg', 'tons', 'gorogoro' etc.). By multiplying the two it is possible to calculate the number of kilograms harvested over the past 12 months (let's label this $C_H$).

Respondents are also asked how much income they gain from the sale of this crop and the units of this income (e.g. 'total income per year', 'price per kg', 'price per gorogoro' etc.). This can either be used to calculate total income per year or the income per kg (let's label these $I_{yr}$ and $I_{kg}$ respectively).

Using these variables it is necessary to calculate: number of kg sold ($C_s$), number of kg consumed ($C_c$), price per kilo ($I_{kg}$), income per year ($I_{yr}$), value controlled by the man ($Ctr_M$), value controlled by the woman ($Ctr_W$) and the values of the crops which were consumed ($V_{cons}$). These calculations mainly rely on proportion responses. The respondent is asked what proportion of the harvest was consumed ($prop_{cons}$) and what proportion was sold ($prop_{sold}$), where none=0, little=0.1, under half=0.2, half=0.5, most=0.7, all=0.9. Therefore:

\begin{equation} C_c = prop_{cons}C_H (#eq:cropconsume) \end{equation}

\begin{equation} C_s = prop_{sold}C_H (#eq:cropsold) \end{equation}

Using the amount sold ($C_S$) it is possible to work out income per kg and price per kilo ($I_{kg}$) and income per year ($I_{yr}$), depending on which sale units were given by the respondent:

\begin{equation} I_{kg} = \dfrac{P_{yr}}{C_s} (#eq:cropincomeperkg) \end{equation}

The price per kg is then averaged across the whole project and verified against FAOstat prices, or verified by field staff. Please not this is step 2 of the indicator calculations. Price verification then makes it possible to calculate the value of crop consumed ($V_{cons}$):

\begin{equation} V_{cons} = P_{kg}{C_c} (#eq:valueconsumed) \end{equation}

Finally, the value controlled by the female ($Ctr{W}$) and the male ($Ctr_M$) in the household can be calculated. The respondent is asked what proportion of the crop consumed is controlled by the male ($Ctr_{Mcons}$) or the woman ($Ctr_{Wcons}$), where none=0, little=0.1, under half=0.2, half=0.5, most=0.7, all=0.9. The respondent is then asked what proportion of the income from the crop sale is controlled by the male ($Ctr_{IM}$) or the woman ($Ctr_{IW}$). Which finally gives:

\begin{equation} Ctr_M= Ctr_{Mcons}V_{cons} + (Ctr_{IM}I_{yr}) (#eq:controlmale) \end{equation}

\begin{equation} Ctr_W= Ctr_{Wcons}V_{cons} + (Ctr_{IW}I_{yr}) (#eq:controlfemale) \end{equation}

This style of proportion calculation is used throughout the indicator calculations, for further details please see individual explanations of each indicator (below).

Indicators

There are a lot of indicators generated in the RHoMIS dataset. In the final "indicator" table, we have a few key indicators, such as total income, household size, and total land cultivated. For complex indicators, such as total income, we have to calculate a series of intermediate indicators to reach the final income (e.g. crop harvested, amount sold, income per kg for that crop). The table below summarises all of the indicators (and intermediate indicators), calculated for core RHoMIS outputs.

In this table you will find:

Indicator Name: The name of the indicator
Output Format: The format for the outputted indicator. If "column", then the indicator will be found as a column in the final indicator table. If "loop", the indicator will be found across multiple columns in the processed dataset (e.g "crop_name_1", "crop_name_2", "crop_name_3"...).
Description: A description of the indicators required
Individual Columns Required: Which individual columns in the processed data are needed to calculate the indicator (e.g. for land_cultivated_ha, we rely on the columns "landcultivated" and "unitland")
Loop Columns Required: Which "looped" columns are needed to calculate the indicator (e.g. for "crop_harvest_kg_per_year_1", "crop_harvest_kg_per_year_2"... we need the columns "crop_yield_1" "crop_yield_2", "crop_yield_3"... and "crop_yield_units_1", "crop_yield_units_2", "crop_yield_units_3"...)
Conversion Tables Required: Many indicators rely on conversion tables. For example "crop_yield_units" come as strings (e.g. "sacks_50kg", "carts"). Conversion tables are used to switch between strings and numeric conversion factors. In this column we specify which conversion factors are required.
API Data Required: Some indicators rely on external APIs, for example to obtain currency conversions. This column specifies which API data is needed.
Indicators Required: Some indicators rely on other intermediate indicators. For example, total income relies on crop income, livestock income, and off-farm income. Here we specify which indicators an indicator depends on.
Function Calculated: Which function in the RHoMIS R-package is used to calculate the indicator
Search Term: A unique search term which can be used to identify the point where the indicator is calculated within the function.
Dependency Diagram: A link to a diagram which illustrates how the indicator is connected to other indicators/columns.

If you would like to see the individual questions, please download a copy of the RHoMIS core survey

# library("widgetframe")
library(DT)
dir.create('./_book/images/indicator_plots/', recursive = T,showWarnings = F)

data_table <- indicator_mapping_to_df()
data_table$dependency_diagram <- paste0('<a href="./images/indicator_plots/',data_table$indicator_name,'.html" target="_blank" >',data_table$indicator_name,' diagram</a>') 
DT::datatable(data_table,
              extensions = 'Buttons',
              escape = FALSE,
              options = list(

                  dom = 'Blfrtip',
                  scrollX = TRUE,
                  buttons = list(
                      'copy',
                      list(
                          extend = 'collection',
                          buttons = c('csv', 'excel'),
                          text = 'Download'
                      )
                  )

                 ))

for(indicator_index in c(1:length(indicator_list))){

    indicator <- indicator_list[[indicator_index]]

    plot <- plot_dependency_network(indicator$indicator_name)

    # Saving the widget
    htmlwidgets::saveWidget(plot,
                            file =      paste0("./_book/images/indicator_plots/",indicator$indicator_name,'.html'),
                            selfcontained = F,
                            libdir = "lib")

}

dir.create('./_book/files/', recursive = T,showWarnings = F)

invisible(file.copy("./files/RHoMIS_Core_v1.6.xlsx", './_book/files/RHoMIS_Core_v1.6.xlsx'))