# **3.1 Multidimensional poverty**

We create a multidimensional poverty (MDP) index inspired by the OPHI MDP based on a synthetic household survey dataset (SHSD) supplemented by three other variables (see below).

# **_3.1.1 Synthetic household survey data_**

Recently, a synthetic household survey dataset (SHSD), representing the location (latitude/longitude) and socioeconomic characteristics of almost 43 million people (10.25 million households) across 19 districts and 150 Upazilas in the coastal zone, was developed using both detailed Census data, household survey microdata and spatial microsimulation techniques (Rubinyi et al., 2022). The SHSD provides the best source of information on the deprivations that Bangladeshi households face on a high spatial resolution, and includes information on housing types, access to water, energy and sanitation, and educational data. The details on the SHSD are included in Appendix A; here we only describe how we have constructed the MDP indicators that we use throughout our work. The original SHSD is based on Census 2011 data, which are projected to 2020 to take into consideration that the population has grown and households have experienced improved living standards over this 10-years period, and hence, face lower MDP.

# **_3.1.2 Multidimensional poverty indicators_**

We construct a new MDP index for the coastal zone of Bangladesh based on nine indicators (see Table 1). The MDP combines variables across the different dimensions that contribute to a households’ wellbeing. These variables are grouped into 3 categories (Education, Living standards, Health) in line with the OPHI MDP. Six of the variables from the MDP can be generated from the SHSD. However, the SHSD still captures only a limited number of dimensions of poverty, for example it does not provide information on health indicators. As such, two additional variables are derived from the accessibility analysis (Section 3.4), access to hospital and health facilities, and another measure, child stunting, is derived from the ‘Multiple Indicators Cluster Surveys’ (MICS) survey dataset using a small area estimation (SAE). SAE uses a regression formulation to distribute aggregated household information to a higher spatial resolution (e.g. from a national to a regional level), either within a survey data (using both aggregated and microdata) or across different surveys (Elbers et al., 2003). Here, we use a regression formulation using overlapping household information in the MICS data and the synthetic household data to construct one variable that is captured in the MICS data (child stunting) but not in the synthetic data (see Appendix B for details). We acknowledge that these variables covering household health status are an incomplete measure of health. We end up with nine variables to construct the MDP index as shown in Table 1.

In line with the OPHI MDP formulation, we also apply a weighting factor to the different variables, such that the three groups contribute equally to the MDP. In the end, the household’s MDP score is the weighted sum of the individual deprivations, which scales between 0 and 1.

Based on the index created, we define two levels of MDP:

- **_Moderate MDP:_** _a household has an MDP of 1/3 or higher_

- **_Extreme MDP:_** _a household has an MDP of 1/2 or higher_

12