Leave no one behind (LNOB) is the central transformative promise of the 2030 Agenda. It compels development actors to consider the furthest behind first and to tackle the discrimination and exclusion that drive the inequalities people experience. Within Development Initiatives’ (DI’s) Poverty and Inequality (P&I) programme, we use our expertise in data and evidence to produce outputs that support our partners and allies to better understand who has been left behind, in what ways, and why.

DI’s LNOB assessments have been developed to apply a systematic methodology that:

1. Identifies and reviews relevant existing data.
2. Analyses that data to answer a locally relevant and targeted policy question.

During 2022 and 2023, four assessments were conducted in Kenya, Uganda, Benin and Nepal. Each assessment had a distinct focus that was identified and developed with local partners.

The LNOB assessment in Nepal sought to understand data and data infrastructure at the municipal level, considering how data can be used to inform local decision-making to tackle poverty and inequality. This approach was applied in two municipalities: Tulsipur and Simta.

This report presents the first part of the LNOB assessment in Simta. It is based on DI’s data landscaping approach and assesses the range, quality and utility of existing data that can potentially inform issues relating to poverty and inequality in the municipality. It also assesses and makes recommendations about the underlying factors that could strengthen Simta’s data ecosystem and enable improved and accessible evidence to be available in the future.

In November 2022, DI and Backward Society Education (BASE) held a co-creation workshop in Simta, which was attended by representatives from Simta’s municipal government. In the co-creation workshop, stakeholders identified priority research questions and discussed the methodological approach. Based on this, DI and BASE adapted DI’s general analytical framework for data landscaping in line with the set parameters. The team then conducted desk-based reviews of grey literature and face-to-face key informant interviews (KIIs) between December 2022 and January 2023. KIIs were conducted with 18 representatives from nine different departments of the local government. A final dissemination workshop was held in Simta on 22 March 2023 with a total of 27 participants representing various organisations.

Part 1 of this report describes the quantity and quality of data included in the data inventory (available as a separate Excel file). Part 2 describes how this data is used in the municipality, Part 3 reviews the strength of the poverty and inequality data ecosystem as a whole, beyond the properties of individual data sources, and Part 4 provides recommendations for strengthening the local data ecosystem.

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In Simta, the study team identified nine data systems that produce information of interest to a poverty and inequality analysis: five administrative data systems, two surveys and two mixed-methods sources (i.e. unique sources that collate data from administrative systems, official surveys and censuses).

The identified systems produce data on demographics, social protection (e.g. child nutrition grants for Dalit children and senior citizens’ allowance for people over the age of 70), asset ownership, employment, education (e.g. enrolment rates and scholarships), health (e.g. vaccination and nutrition), disaster risk reduction (e.g. damage to housing by flooding), disability (e.g. prevalence), and water, hygiene and sanitation (WASH). The study team was unable to identify data on dimensions of poverty relating to voice and political participation.

The study team tried to identify unofficial sources but could not.^[1]

Inventories of data systems

Disaggregation

In order to inform a leave-no-one-behind approach, it is necessary to identify individual and group-based characteristics that may influence poverty outcomes. To enable this, data must capture variables relating to multiple dimensions of poverty, such as health or access to electricity, but also include particular variables that can allow for disaggregation by characteristics that may be associated with inequality and exclusion within a population, such as gender, age or geography.

Six of the nine data systems produce data disaggregated by geography (i.e. wards), five of the nine data systems produce data disaggregated by age (this does not include the education data available to us, as this was disaggregated by grade or year group), and five of the data systems produce data disaggregated by gender. There remains room for improvement but arguably efforts to produce data disaggregated by these dimensions have been fairly successful, as they feature in datasets produced by more than 50% of the identified data systems.

In contrast, data disaggregated by ‘ethnicity’ is only produced by two of the identified data systems, meaning it is absent from seven.^[2] However, this is not always because systems do not collect data on ethnicity; for example, data on ethnicity is collected by the Employment Management Information System (EMIS) and the Smart Daughter Programme MIS on paper forms, but it is not uploaded to the digital system, and sits unused in storage facilities.

The type of disaggregated data collected least often is (types of) disability data. This data is only collected by one of the identified data systems.

The omissions of these characteristics (e.g. ethnicity and disability) from some datasets, prevents users from being able to use those datasets to generate insights about these dimensions via quantitative analysis (e.g. investigating how disability intersects with health), and severely limits the range of evidence available to them. Collecting and digitally storing disaggregated data across all data systems in Simta would better enable intersectional analysis.

Frequency

Ward offices upload civil registration data (e.g. birth and death registrations) to VERSP-MIS on a daily basis, and Nepal Police also upload data to the DRR portal on a daily basis. This means users can access this information in real time.^[3] Data for the HMIS is updated on a less frequent basis, but it is still satisfactorily regular; facilities collect data on paper forms on a daily basis and upload it to DHIS2 (HMIS) every month.^[4]

Conversely, data for the IEMIS is collected and uploaded to the digital system on a much more infrequent basis. Data is collected once at the beginning of the academic calendar and once again at the end; it is uploaded to the system by the school with support from the rural municipality (which also verifies it at the same time).

Data is collected via the Municipal Profile Survey on an even less frequent basis: every five years. The infrequent scheduling is due to the costs of carrying it out. Therefore, despite the survey’s capacity to produce high-quality statistics, surveys are not administered regularly enough to be relied on as the main source of evidence for local decision-makers.

Data collectors

Training people to work with data means they are less likely to make mistakes that can reduce data reliability.

Most interviewees report that at the municipal level there is a satisfactory number of dedicated staff who are trained to work with data. Conversely, interviewees report that there are not enough dedicated and trained staff to work with data at the ward level; administrative data systems (such as HMIS or VERSP MIS, etc.) capture data at the ward level, and, therefore, if too few people at this level are trained, the reliability of the data being captured is reduced. The health department, which has the strongest level of human resources at the ward level, still reported shortfalls.

Federal departments do conduct periodic training in the municipalities to increase civil servants’ capacities to work with data. Additionally, the Data for Development (D4D) Program, which is funded by the UK government and run by the Asia Foundation, has conducted training programmes to strengthen data literacy in the municipality. Similar investments need to be provided more regularly and to a wider group of stakeholders at the municipal level, and – more pressingly – to the wards.

Metadata

Complete metadata allows actors to understand the data they are working with more promptly and thoroughly, and this encourages data use. Metadata is commonly made available alongside datasets produced by five of the nine data systems included in the inventory. The other four data systems only provide partial or no metadata alongside the datasets they produce. For example, metadata that accompanies disability data does not include the exact date it was initially recorded, nor who collected it. There is a chance that this information is recorded in the paper registers, but it is not digitised.

Open data

In Simta, the only data that is openly available online is through the DRR portal, and from its Municipal Profile Survey, 2075 BS (2018/19) in the form of summary statistics (not microdata). If more data from different sources was made available online, it would allow a variety of users to engage with it for different reasons. Open data could benefit the general public (e.g. people working for civil society organisations (CSOs), non-governmental organisations (NGOs) and private businesses, etc.), as well as enabling increased data sharing between municipal departments.

Discrepancies in disaster data

There are some discrepancies in the disaster risk reduction data depending on the source. For example, the numbers quantifying damage to buildings, schools and agricultural farmland for the same time period are different in the municipality’s report (2022), the data the municipality submitted to the district office, and the data published via the DRR portal. These kinds of variations raise significant concerns about the reliability of the data being produced. Doubts about reliability erode the trust potential users have in the data, and, in turn, low levels of trust can cause potential users to be hesitant about using data.

Poverty and inequality data is used for a number of purposes in Simta. For example, data is used to design skill-training programmes for persons with disabilities and single women; to distribute scholarships to female students; to design budgets and plans in the health department; and to distribute various social security payments.

However, interviewees told us that data use in Simta could be extended (e.g. Simta’s municipal chairperson suggested data-led advocacy initiatives are required to promote the socioeconomic development of marginalised groups), despite these examples of current data use. The primary reasons why data is not used to its full potential are:

• A lack of interest in evidence-informed decision-making
• A lack of digitisation
• Inaccessible data
• Distrust in the quality of some data
• Low levels of data literacy.^[5]

Governance and management

Issues with the foundational components of Simta’s local data ecosystem are unpacked in the following subsections of this report. Some analysis relates entirely to poverty and inequality, whereas the focus of other analysis is more general but equally significant.

Municipal policy on local data

Simta has a fairly comprehensive ‘Data Management Policy’, 2079 BS (2022/23).^[6] It outlines protocol on the protection and management of municipal data, and the use of data to inform decision-making. It also covers data standardisation, storage, ownership, open data and public awareness.

Moreover, Simta also has a ‘Work Distribution Manual’ 2074 BS (2017). This manual delegates many responsibilities relating to data collection and management to municipal departments; a number of these responsibilities intersect with poverty and inequality. For example, it stipulates that poverty alleviation and employment promotion are the responsibility of the economic division, including the ICT-based collection and management of poverty data.

It is clear that Simta’s government has laid a decent policy foundation for the collection and management of local data. However, interviewees report that few people in the municipality know about the policies. Therefore, the next step is to mainstream the policies into the day-to-day activities of the local departments.

ICT infrastructure

Modern information systems are digital and rely on electricity, so there are obvious negative consequences if there is no supply of electricity (or if it is intermittent).

In Simta, parts of the municipality do not have a supply of electricity. In fact, only ward six is completely electrified, whereas there is no electrical supply to wards two and nine at all.^[7] As previously stated, administrative data systems (e.g. the Vital Event Registration and Social Protection or VERSP MIS) capture data at the ward level, therefore a ward with no electricity has to use paper forms (which they pass on to municipal offices to input data digitally), and the benefits of full digitisation are lost (e.g. a reduced risk of anomalies due to restricted fields of input).

Furthermore, modern information systems also rely on internet connectivity. In Simta, ward-level internet connectivity is very weak, and issues with it also extend to municipal-level offices. In 2022, the chairperson of the municipal government stated that electrifying all the wards in the municipality and extending internet connectivity to them is a priority of the local government.^[8] However, significant improvements have not yet been achieved.

Cross-departmental coordination

Whether in the form of a commonly followed data-sharing protocol or through a coordinating body, at present, there are no formalised approaches to cross-departmental data coordination in Simta. Departments often work in silos and, for the most part, even an informal data-sharing culture has not emerged.^[9]

As per the ‘Work Distribution Manual’, Simta should have established a ‘Department of Planning, Monitoring and Data’, which theoretically should encourage and formalise data sharing between departments (see Box A1 in the Annex for more details). However, currently this exists only on paper and not in practice.

The negative consequences of underdeveloped coordination mechanisms are not lost on municipal officials. For example, the lack of guidelines for sharing and using data between departments was understood in the project workshop to be partly responsible for discrepancies in the data obtained from different data sources. Also, the exclusion of ethnicity from various digital systems was partially blamed on there being no standardised processes for uploading data from registers into Excel sheets.

Budget

The study team was unable to identify spending earmarked for data-related activities in any public budgets in Simta, apart from the Department of Education allocating 200,000 Nepali rupees (approximately US$1,500) to train officials to use the Integrated Education Management Information System (IEMIS) and build ICT capacity. Increasing investment in data-related activities in the municipality, especially on those which intersect with poverty and inequality, can lead to more informed decision-making, targeted interventions, and evidence-based policies to address systemic disparities and uplift marginalised communities.

This section outlines top-level policy recommendations based on the findings detailed in parts 1–3.

Data sources:

• Ensure data on ethnicity, which is collected on paper forms, is input into digital systems.
• Explore the possibility of collecting routine education data through administrative systems more frequently (the Integrated Education Management Information System or IEMIS is managed at the federal level, so this will require innovative solutions).
• Train key ward-level officials in basic statistical concepts and the specific digital systems they use.
• Ensure complete metadata is collected and published by the four remaining data systems not currently using it.
• Publish more data on the municipality’s website or through other online platforms.
• Ensure consistent disaster data is accurately shared across sources.

Data use:

• Identify and train key staff so they have the skills to conduct basic data analysis.
• Identify what data key users need and prioritise its publication through online platforms.
• Hold workshops with key officials to promote the value evidence-informed decision-making has in policymaking and public service provision.

Data governance and management:

• Mainstream the ‘Data Management Policy’ and ‘Work Distribution Manual’ into the day-to-day activities of municipal departments and ward offices.
• Complete the full electrification of ward offices and establish a stable internet connection in the municipality and at ward level.
• Establish the ‘Department of Monitoring, Planning and Data’ to design and implement a data-sharing protocol for municipal departments to use.
• Increase the allocation of spending on data-related activities.