Image by Adrienne Henck, 2010 Peace Fellow
  • Report
  • 5 June 2024

Data analysis to leave no one behind in Tulsipur, Nepal

This report analyses the available data in Tulsipur municipality in Nepal and finds that children, women and girls are at risk of being left behind. It makes recommendations that can be used for policymaking to address inequalities and improve the data landscape.

Downloads
Jump to section

In recent years, Nepal has made significant progress in reducing the number of people living in multidimensional poverty, from 30% of the population in 2014 to 17% in 2019. This represents 3.1 million citizens lifted out of multidimensional poverty. However, while progress at the national level has been substantial, there is a risk that certain groups could be left behind.

In order to gain an understanding of who these groups might be and the data available to inform local decision-making, Development Initiatives (DI) has carried out a data landscaping and analysis assessment in two municipalities in Nepal – Simta and Tulsipur. These assessments form part of DI’s body of work in support of the Agenda 2030 commitment to leave no one behind (LNOB).

We first conducted a data landscaping assessment of the availability of data and evidence in the two municipalities. This report then analyses the data collated in the initial data landscaping assessment in Tulsipur, presents the findings and makes recommendations. It largely uses data from the Nepal National Population and Housing Census 2021 (‘the 2021 Census’) and local data systems and processes from 2018 to 2022. You can read more about the data sources used in this report in Part 2.

The analysis in this report helps identify those at risk of being left behind – namely women and children. It provides information and makes recommendations that can be used to inform local decision-making to tackle poverty, inequalities, and improve data quality and systems for further evidence. Furthermore, it shows how local data can help identify groups being left behind and that improved data is needed to fully understand inequalities experienced by people of ethnic minorities and persons with disabilities (PWDs).

The Executive summary presents the key findings. Part 1 introduces the report, and Part 2 explains the data sources used and data limitations. Part 3 outlines the municipality’s demographic and living standards. Part 4 goes on to identify groups shown to be at risk of being left behind, with a focus on women and children. Part 5 uses available data provided by the municipality to review two social protection programmes and their ability to target and aid groups most at need. Finally, Part 6 summarises the report findings and provides recommendations for data improvement and use in local decision-making.

Download the full report. You can also read our report about Simta municipality.


► Read our initial report on data landscaping in Simta, Nepal

► Discover more about DI's data landscaping approach

► Sign up to our newsletter


anchor
Share section

Executive summary

In a matter of five years, the proportion of people living in multidimensional poverty in Nepal has significantly decreased from 30% in 2014 to 17% in 2019.[1] This substantial progress meant that 3.1 million citizens were lifted out of multidimensional poverty, and both the social and economic living standards of millions of people were improved nationwide. However, while progress at the national level has been substantial, there is a risk that certain populations and communities could be left behind if this progress is not shared. In support of the Agenda 2030 commitment to leave no one behind (LNOB), DI conducted a study to assess the availability of data and evidence in two municipalities in Nepal – Tulsipur and Simta. This report presents the findings in relation to Tulsipur municipality and aims to support government partners in understanding who is at risk of being left behind, in what ways, and why.

This report analyses the data collated in the initial data landscaping assessment in Tulsipur municipality, largely relying on data from the Nepal National Population and Housing Census 2021 (‘the 2021 Census’) and local data systems and processes between 2018 and 2022. It seeks to identify those at risk of being left behind and provide information using existing municipal data which can be used to inform local decision-making to tackle poverty and inequalities. This report identifies that women and children are being left behind, while additional data collection and research are required to fully understand inequalities experienced by people of ethnic minorities and persons with disabilities (PWDs).

Key findings

1. Some notable comparisons can be made in the living standards between urban and rural administrative wards in Tulsipur

Tulsipur is a sub-metropolitan city and municipality in the Lumbini province of mid-west Nepal with a population of 179,755. In 2022/23, the National Living Standard Survey IV recorded 24% of the population in Lumbini to be in poverty – higher than the 20% national rate. The 385 km² municipality is composed of 19 administrative wards that span across commercial, agricultural and forest land. Households in more ‘urban’ wards (Wards 5−9) were found to have slightly better living conditions compared to ‘rural’ households (Wards 1−4 and 10−19).

  • Households in rural administrative wards are at greater risk of flooding. 29% of households in rural wards are at medium or high risk of flood, compared to 18% of urban households.
  • Rural households are made with lower-quality building materials. The majority of households in urban wards were found to be made from cement floor, roof, and walls, while rural households mostly consist of a combination of mud or cement floor and walls, and galvanised sheet roofs.
  • Urban households have greater access to amenities such as computers, refrigerators, and vehicles. 54% of urban households claimed to have internet in comparison to 29% of rural households. Urban households were recorded to have greater levels of access to almost all amenities included in the survey.
  • Urban households have greater access to better-quality roads. 9% of rural households have access to pitch roads and 31% to dirt roads. This is in comparison with 44% and 17% of urban households having access to pitch and dirt roads respectively.

2. Women face compounded burdens that place them at risk of poverty and socioeconomic inequalities

This report found clear evidence that women are one group being left behind in Tulsipur. With lower education enrolment, literacy and economic participation than men, and higher rates of unemployment, early marriage, and single parenting, women face multiple burdens that place them further at risk of poverty. There are some programmes and social security systems in place to support certain groups of women, but their impact is not always clear or fully measured.

  • Among some age groups, women were found to have less education and lower attendance than men. 62% of women between the ages of 20 to 39 were found to have completed school to Grade 10 or higher, which is comparable to men of the same age group – 63%. Young girls and women between the ages of 5 and 25 report just slightly lower school attendance – 74% – than boys and men of the same age group – 79%. The difference is greater among older age groups as 47% of women over the age of 40 report never having attended education in comparison to 30% of men the same age.
  • Literacy levels are significantly lower for older women than men. Literacy rates are very high for both boys/men and girls/women under the age of 20 at close to 100%. However, literacy rates among the older generations vary a lot by gender. For example, women between the ages of 65 and 69 have a literacy rate of 16% in comparison to 52% of men in the same age group. These findings do not differ very much from the national averages.
  • Women in Tulsipur have lower economic participation and higher levels of unemployment than men. 45% of female citizens over the age of 10 were recorded as economically inactive. Of those who are economically active, 10.5% are unemployed and seeking work. By comparison, 34% of the male population over the age of 10 is economically inactive and 7.5% are unemployed.
  • The Prime Minister’s Employment Program (PMEP) helps unemployed women find work opportunities but has limited coverage. The PMEP specifically targets unemployed women to help decrease the number of women in unemployment. In Tulsipur, two-thirds of applicants were female and made up 77% of all successful applicants. However, of the 2,723 unemployed women identified in the municipality, only 399 were successful applicants, the equivalent of 15%.
  • Single mothers look after a large portion of the children in the municipality. One in four children in the municipality was found to live with a single mother.
  • Social security allowances aid widowed and senior single women by providing cash transfer allowances. In 2021/22, over 3,702 allowances of NPR 31,920 were granted to widowed and senior single women in Tulsipur.
  • Early marriage is a prevalent trend likely hindering educational opportunities for young women and girls. While there is limited data to measure the impact of marriage and childcare on education, marriage statistics show that 9.3% of married women today married before the age of 15, and 43% before the age of 18. Of those currently not in education (both male and female), 31% claim to leave due to marriage and childcare responsibilities.

3. Children and adolescents receive very little social assistance despite making up almost half of Nepal’s poorest people

In Nepal, children and adolescents under 18 make up the largest share of people living in multidimensional poverty. According to national data, 44% of all Nepalese people living in poverty, the equivalent to 2.2 million citizens, are children. In other words, one in four children in Nepal live in multidimensional poverty. Over a third of Tulsipur’s population are children and adolescents.

  • Only 4% of all funds provided through social assistance allowances reached children and adolescents under the age of 18. Without any cash assistance to most children in the municipality, especially those between the ages of 5 and 18 that receive near to no allowances, families face the near full cost of educational materials and nutrition with limited in-kind programmes available.
  • A significant number of adolescents do not pursue higher levels of education. 83% of the population between the ages of 18 and 25 were found to not be in education. Of all 5-to-25-year-olds out of school, marriage and childcare responsibilities were cited as the most common reason for leaving schooling.
  • One in four children under the age of 18 live with a single parent. This places them at higher risk of being in poverty, as well as being involved in child labour to help bring additional income to their household.
  • A considerable number of children are involved in child labour. In Tulsipur, 21% of children aged 10 to 14 report to have done some sort of formal work in the last year.

4. A lack of quality, timely data makes it difficult to understand socioeconomic inequalities and aid those most vulnerable

National censuses are conducted once every ten years in Nepal. This report largely relied on data from the 2021 national census for municipal-level analysis. With limited data gathered from other sources, timely analysis for local decision-making would be difficult to conduct.

  • The lack of disaggregated datasets limits comparison across variables. With almost all data published being aggregated, census data included, comparison of additional insight in child, gender, and other potential groups at risk could not be analysed.
  • Limited datasets available. Low-quality data, or in some cases no data, on variables such as income, health, gender-based violence, and nutrition were found in the collated data.
  • Missing metadata and additional information. While not totally limiting, inclusion of metadata could have simplified or improved the analysis process.
  • Incomplete tracking of local social security programs. Only two social security programs (PMEP and social security allowances (SSA)) were found to have available data despite there likely being other cash and in-kind social security programs available.