In 2018, the share of the urban population living in slums in Nepal amounted to just over 49 percent. This was a decrease from 2005, in which almost 61 percent of the urban population in Nepal were living in slums.

Nepal NP: Population Living in Slums: % of Urban Population data was reported at 54.300 % in 2014. This records a decrease from the previous number of 58.100 % for 2009. Nepal NP: Population Living in Slums: % of Urban Population data is updated yearly, averaging 60.700 % from Dec 1990 (Median) to 2014, with 7 observations. The data reached an all-time high of 70.600 % in 1990 and a record low of 54.300 % in 2014. Nepal NP: Population Living in Slums: % of Urban Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Nepal – Table NP.World Bank: Population and Urbanization Statistics. Population living in slums is the proportion of the urban population living in slum households. A slum household is defined as a group of individuals living under the same roof lacking one or more of the following conditions: access to improved water, access to improved sanitation, sufficient living area, and durability of housing.; ; UN HABITAT, retrieved from the United Nation's Millennium Development Goals database. Data are available at : http://mdgs.un.org/; Weighted Average;

Additional file 2. Dataset.

Qualitative data collected through focus group discussions and semi-structured interviews. Questions included respondent background (e.g. place of origin, family structure), experiences of pregnancy and childbirth, maternity care, and choice of health care provider. Purposive sampling of women aged 18-49 based on participant characteristics (e.g. religion, parity), choice of health care provider (none, public or private), and emerging themes.

This data collection is the forth out of four collections, part of Phase B. The other 3 collections are linked below in ‘Related resources’.
Our qualitative data collection was done in two phases. In Phase A of the project, we sought to understand how socioeconomic and sociodemographic status of women affects women’s group attendance. Phase A of the project was conducted in all trial sites. The data are reported under ‘part 1’. In Phase B of the project, we primarily sought to explain our quantitative findings relating to the equity impact of the women’s groups intervention on neonatal mortality and health behaviours. This was done only in those sites where the trials had shown a substantial and statistically significant impact on neonatal mortality (rural India, Nepal-Makwanpur, Bangladesh, and Malawi). The data are reported under ‘part 3’. We conducted other, smaller, studies as part of Phase B of our project in the sites where the trial findings where either not yet published at the time of our study (Nepal-Dhanusha) or where the trial showed no impact on neonatal mortality (urban India). The data for these smaller, site-specific studies, are reported under ‘part 2’ (Nepal-Dhanusha) and ‘part 4’ (urban India).

Progress towards the Millennium Development Goals (MDGs) has been uneven. Poor and otherwise disadvantaged groups lag behind their more fortunate compatriots for most MDGs.To make things worse, effective interventions are known, but rarely reach those who need them most. Unfortunately, little is known about how to effectively reach poor and otherwise disadvantaged groups, and how to address socio-economic inequalities in mortality. The project aims to fill these gaps by generating evidence on: (1) how socio-economic inequalities translate into inequalities in newborn and maternal mortality; (2) how to address the exclusion of poor and otherwise disadvantaged groups from efforts to achieve the MDGs; (3) how to reduce socio-economic inequalities in maternal and newborn mortality. Data from 6 surveillance sites in India, Nepal, Bangladesh and Malawi are used (combined population > 2 million); Information on birth outcomes; socio-economic position, health care use and home care practices are used to describe and explain mortality inequalities. Data from randomized controlled trials of women’s group interventions are used to evaluate the equity impact of community mobilization. The project actively engages with and learns from stakeholders, drawing on their experiences regarding what works to ensure an equitable improvement in newborn and maternal health. We used existing quantitative data from randomised controlled trials of participatory women’s groups to reduce neonatal mortality. The intervention consisted of women’s groups, facilitated by a local woman. The facilitator led the groups through a participatory action cycle, in which they identified and prioritised maternal and newborn health problems in the community, collectively selected relevant strategies to address them, implemented the strategies, and evaluated the results. These trials were conducted in six large demographic surveillance sites, in India (Mumbai and Orissa & Jharkhand), rural Nepal (Dhanusha, and Makwanpur districts), rural Bangladesh and rural Malawi. The data were collected through interviews with women that have given birth in the study sites. In addition, our project collected new qualitative data in the trial sites, using focus group discussions and semi-structured interviews, to help understand our quantitative findings. The data deposited in this archive, pertain to this qualitative data collection.

Summary of data sources.

Characteristics of the women interviewed.

Urbanisation brings with it rapid socio-economic change with volatile livelihoods and unstable ownership of assets. Yet, current measures of wealth are based predominantly on static livelihoods found in rural areas. We sought to assess the extent to which seven common measures of wealth appropriately capture vulnerability to poverty in urban areas. We then sought to develop a measure that captures the characteristics of one urban area in Nepal. We collected and analysed data from 1,180 households collected during a survey conducted between November 2017 and January 2018 and designed to be representative of the Kathmandu valley. A separate survey of a sub set of households was conducted using participatory qualitative methods in slum and non-slum neighbourhoods. A series of currently used indices of deprivation were calculated from questionnaire data. We used bivariate statistical methods to examine the association between each index and identify characteristics of poor and non-poor. Qualitative data was used to identify characteristics of poverty from the perspective of urban poor communities which were used to construct an Urban Poverty Index that combined asset and consumption focused context specific measures of poverty that could be proxied by easily measured indicators as assessed through multivariate modelling. We found a strong but not perfect association between each measure of poverty. There was disagreement when comparing the consumption and deprivation index on the classification of 19% of the sample. Choice of short-term monetary and longer-term capital approaches accounted for much of the difference. Those who reported migrating due to economic necessity were most likely to be categorised as poor. A combined index was developed to capture these dimension of poverty and understand urban vulnerability. A second version of the index was constructed that can be computed using a smaller range of variables to identify those in poverty. Current measures may hide important aspects of urban poverty. Those who migrate out of economic necessity are particularly vulnerable. A composite index of socioeconomic status helps to capture the complex nature of economic vulnerability.

NP：生活在贫民窟的人口：占城镇人口百分比在12-01-2014达54.300%，相较于12-01-2009的58.100%有所下降。NP：生活在贫民窟的人口：占城镇人口百分比数据按年更新，12-01-1990至12-01-2014期间平均值为60.700%，共7份观测结果。该数据的历史最高值出现于12-01-1990，达70.600%，而历史最低值则出现于12-01-2014，为54.300%。CEIC提供的NP：生活在贫民窟的人口：占城镇人口百分比数据处于定期更新的状态，数据来源于World Bank，数据归类于Global Database的尼泊尔 – 表 NP.世界银行：人口和城市化进程统计。

Main features of poor, medium and better-off households from qualitative data and proxy quantitative indicators from household survey.

Migration status of households by vulnerability category (number of observations and 95% confidence intervals in brackets).

Measures of vulnerability computed from household data.

Characteristics of sample using Urban Poverty Index.

Predictors of poverty status [1].

Wealth characteristics of migrant and settled population.

Association of socio-demographic, environmental, biological and anthropometric characteristics, and developmental status of children (n = 165).

Environmental characteristics.

Biological and anthropometric characteristics.

Factors associated with developmental status of children (n = 165).