Devinit-尼泊尔LNOB评估：Simta市的数据景观（英）-2023-WN6.pdf
LNOB assessment Nepal:Data landscaping in Simta municipality Report June 2023 LNOB assessment Nepal:Data landscaping in Simta municipality/devinit.org 1 Contents Introduction.2 Part 1:Simtas poverty and inequality data inventory.4 Inventories of data systems.4 Disaggregation.5 Frequency.6 Data collectors.7 Metadata.7 Open data.8 Discrepancies in disaster data.8 Part 2:The use of poverty and inequality data in Simta.9 Part 3:The foundations of Simtas poverty and inequality data ecosystem.10 Governance and management.10 Municipal policy on local data.10 ICT infrastructure.10 Cross-departmental coordination.11 Budget.11 Part 4:Recommendations.12 Data sources:.12 Data use:.12 Data governance and management:.12 Annex.13 Notes.18 LNOB assessment Nepal:Data landscaping in Simta municipality/devinit.org 2 Introduction 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(DIs)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.DIs 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 DIs 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 Simtas 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 Simtas municipal government.In the co-creation workshop,stakeholders identified priority research questions and discussed the methodological approach.Based on this,DI and BASE adapted DIs 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.Part 2 describes how this data is used in the municipality,Part 3 reviews the LNOB assessment Nepal:Data landscaping in Simta municipality/devinit.org 3 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.LNOB assessment Nepal:Data landscaping in Simta municipality/devinit.org 4 Part 1:Simtas poverty and inequality data inventory 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 Table 1:Inventory of five administrative data systems Data system What data is collected?DRR Portal Type of incident(e.g.fire,animal incident,storm),location of incident,number of people impacted by an incident and how(e.g.killed or injured),damage to infrastructure.Employment Management Information System(EMIS)Information about applicants(ethnicity and gender,etc.).Health Management Information System(HMIS)Information on maternal and neonatal health,nutrition,vaccination and immunisation,and more.Integrated Education Management Information System(IEMIS)Information on students,teachers and other staff.VERSP MIS Information on births,deaths,marriages,divorces and migration.LNOB assessment Nepal:Data landscaping in Simta municipality/devinit.org 5 Table 2:Inventory of two mixed-method sources Data system What data is collected?Disability identity Card Classifications of disabilities Smart Daughter Programme Information about family members:names,addresses and place of birth,etc.Table 3:Inventory of two official surveys Data system What data is collected?Disaster Risk Survey Type of hazards and impacts Municipal Profile Survey Overall household survey including demographic profile,socioeconomic profile,infrastructure,occupation,unemployment,etc.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.LNOB assessment Nepal:Data landscaping in Simta municipality/devinit.org 6 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.Figure 1:There are low levels of disaggregated ethnicity and disability data in Simtas data systems Number of data systems by type of disaggregation Source:DI,2023.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“There is a dedicated DHIS2 user in health department.Each month,the focal person from health facilities meets and share data which is 01234567DisabilityEthnicityAgeGenderWardNumber of data systemsTypes of disaggregation LNOB assessment Nepal:Data landscaping in Simta municipality/devinit.org 7 entered in DHIS2.The data is reviewed half yearly to see if targets are met.”Interviewee employed in Simtas health department,2022 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 surveys 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.LNOB assessment Nepal:Data landscaping in Simta municipality/devinit.org 8 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.Figure 2:The majority of data in Simta is not openly accessible Proportions of open-access and inaccessible data in Simta Source:DI,2023.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 municipalitys 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.Inaccessible data78%Open-access microdata11%Open-access aggregate statistics11%LNOB assessment Nepal:Data landscaping in Simta municipality/devinit.org 9 Part 2:The use of poverty and inequality data in Simta 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.“The health department makes decisions based on evidence and data.They organise meetings with the management committee and discuss the challenges and problems which are then discussed with the planning department.”Interviewee employed in Simtas health department,2022 However,interviewees told us that data use in Simta could be extended(e.g.Simtas 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 LNOB assessment Nepal:Data landscaping in Simta municipality/devinit.org 10 Part 3:The foundations of Simtas poverty and inequality data ecosystem Governance and management Issues with the foundational components of Simtas 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 standardisa
