R/MSNA_HC.R
MSNA_HC.RdExtract from the "COX'S BAZAR HOST COMMUNITY (HC) MULTI-SECTOR NEEDS ASSESSMENT (MSNA) - HOUSEHOLD DATA", December 2018. Probability sample of 2855 households in 11 communes ("Unions") in two sub-districts ("Upazillas") in south-eastern Bangladesh. Basis for the calculation of a living standards deprivation index, on which households differ by gender of household heads and commune of residence.
MSNA_HCA data frame with 2855 obs. in 15 variables:
Household ID: integer
Sampling weights: float, 11 distinct values (1 per Union), range: [0.5694934, 1.396798]
Upazilla (sub-district): two distinct values: "Teknaf", "Ukhiya": character string / factor
Union (commune): 11 distinct values. "Baharchhara", "Nhilla", "Sabrang", "Teknaf", "Teknaf Paurashava", and "Whykong" Unions in Teknaf Upazilla, as well as "Haldia Palong", "Jalia Palong", "Palong Khali", "Raja Palong", and "Ratna Palong" Unions in Ukhiya Upazilla: character string / factor
Gender of household head: female or male: character string / factor
Food deprivation indicator, calculated from the original Food Consumption Scores: float, range [0.0669643, 1]
Indicator of less favorable livelihoods combinations: Ordinal, rescaled to: 0.25 0.50 0.75 1.00. Of nine livelihood types that the MSNA observed, five - domestic work, non-agricultural work, fishing, small business, and remittances - are significantly associated with the scores of household food consumption. Across the sample households, the five types appeared in 23 combinations. These were mapped to a scale, with four levels, of increasingly less favorable combinations, reflected in a (nearly linear) decrease in the predicted food consumption scores. Because of that nearly linear effect, this indicator is treated as interval-level variable.
Probability of living in a worse shelter than others: Four distinct values: 0.0696, 0.3204, 0.6645, 0.9137. Household dwellings are classified by construction types, which reflect decreasing levels of comfort and value: Pucca (highest, best), Semi-pucca, Kutcha, and Jhuprie (lowest, worst). For lack of finer grading or market value data, it is assumed that within each class, half of the households are slightly worse off than the other half. Thus, over the four classes, for a given household one of the noted four distinct values is the probability of occupying a dwelling worse than the other households in the population, calculated as the sum of proportions of households in types better than its own (if any) plus half of the proportion in its own (this measure is known as the "ridit"; see https://en.wikipedia.org/wiki/Ridit_scoring).
No year-round access to improved water source: binary / 0 or 1
Problems encountered when collecting water: binary / 0 or 1
Walking to and from the water source takes more than 30 minutes: binary / 0 or 1
Trash visible: binary / 0 or 1
Faeces visible: binary / 0 or 1
Stagnant water visible: binary / 0 or 1
Household members defecate outside home: binary / 0 or 1
ISCG (Inter Sector Coordination Group) / REACH / ACAPS-NPM, Cox Bazar, Bangladesh, December 2018, published with the provision: "This tool is made available to all staff and partners of the ISCG, and to the general public as a support tool for strategy and programming in the humanitarian response in Bangladesh and other related purposes only. Extracts from the information from this tool may be reviewed, reproduced or translated for the above-mentioned purposes, but are not for sale or for use in conjunction with commercial purposes." Original food consumptions scores calculated by REACH. Indicators ls_1_food, ls_2_livelihood and ls_3_shelter calculated by Aldo Benini, from a cleaned dataset compiled by Sudeep Shrestha, ACAPS.
The continuous indicators ls_1_food, ls_2_livelihood and ls_3_shelter measure deprivation in three living standards (ls) components, each one corresponding to a particular humanitarian sector. The items with prefixes water_ and sanit_ are observed in the water, respectively sanitation sub-sectors of the Water, Sanitation and Hygiene (WASH) sector.
This dataset has been chosen to demonstrate a two-level deprivation model using the function mdepriv, with the Betti-Verma double weighting rule operating at both levels. At the lower level, the seven binary WASH items are aggregated to a continuous WASH deprivation indicator, ls_4_WASH. To equalize subsector contributions, water_ and sanit_ items are grouped in different dimensions. At the higher level, ls_4_WASH is combined with the other three ls-indicators and aggregated to a living standards deprivation index.
In this humanitarian context, there is no basis to consider one or the other water or sanitation problems more or less important on the basis of their different prevalence.
Therefore, in aggregating the seven binary water_ and sanit_ items, mdepriv takes the arguments wa = "equal".
Moreover, wb = "mixed" has the effect to reduce weights on more redundant items
(wb = "diagonal" would be neutral to redundancy; and wb = "pearson" would underrate the strength of correlations between binary items).
This model returns the deprivation scores in the variable "ls_4_WASH".
At the second level, in the aggregation of the four continuous ls-indicators (ls_1_food, ls_2_livelihood, ls_3_shelter, ls_4_WASH),
the default Betti-Verma method is used, by setting method = "bv" in the function mdepriv.
This activates both mechanisms of the double-weighting scheme - rewarding more discriminating indicators with higher weights,
and penalizing redundant ones with lower weights.