Devinit-尼泊尔LNOB评估：Tulsipur市的景观数据（英）-2023-WN6.pdf

LNOB Nepal:Data landscaping in Tulsipur municipality/devinit.org 0 LNOB assessment Nepal:Data landscaping in Tulsipur municipality Report June 2023 LNOB Nepal:Data landscaping in Tulsipur municipality/devinit.org 1 Contents Introduction.2 Part 1:Tulsipurs poverty and inequality data inventory.4 Inventories of data systems in Tulispur.4 Disaggregation.5 Frequency.7 Data sharing and open data.8 Metadata.9 Part 2:The use of poverty and inequality data in Tulsipur.10 Part 3:The foundations of Tulsipurs poverty and inequality data ecosystem.11 Governance and management.11 ICT infrastructure and human resources.11 Cross-departmental coordination.12 Legislation and policy.12 Budget.12 Part 4.14 Data sources:.14 Data use:.14 Data governance and management:.14 Annex.16 Notes.20 LNOB Nepal:Data landscaping in Tulsipur 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 inequality.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 existing 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 reduce poverty and inequality.This approach was applied in two municipalities:Tulsipur and Simta.This report presents the first part of the LNOB assessment in Tulsipur.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 Tulsipurs 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 Tulsipur.This was attended by representatives from Tulsipurs municipal government,the Asia Foundation,the Sewa Foundation,the NGO Federation of Nepal,Didi Ghar and Apanaga Samhua.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 10 actors from 10 different departments of the local government.A final dissemination workshop was held in Tulsipur on 20 March 2023,with a total of 34 participants representing various organisations.LNOB Nepal:Data landscaping in Tulsipur municipality/devinit.org 3 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 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 Nepal:Data landscaping in Tulsipur municipality/devinit.org 4 Part 1:Tulsipurs poverty and inequality data inventory The study team identified 11 data systems that provide information on poverty and inequality in Tulsipur;five administrative data systems,one survey and five mixed-method sources(i.e.unique sources that collate data from administrative systems,official surveys and censuses).The identified systems produce data on social security payments(e.g.allowances for children and senior citizens);employment;asset ownership;education(e.g.enrolment rates and not-in-school numbers);health(e.g.vaccination and nutrition);violence against women;and disability(e.g.prevalence).The study team was unable to identify data on dimensions of poverty relating to voice and political participation.Although the study team tried to identify unofficial data sources,they were unable to.1 Inventories of data systems in Tulsipur Table 1:Inventory of Tulsipurs 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 neo-natal health,nutrition,vaccination and immunisation,and more.Integrated Education Management Information System(IEMIS)Information on students,teachers and other staff.Vital Event Registration and Social Protection Information on births,deaths,marriages,divorces and migration.LNOB Nepal:Data landscaping in Tulsipur municipality/devinit.org 5 management information system(VERSP MIS)Table 2:Inventory of Tulsipurs five mixed-method sources Data system What data is collected?Disability identity Card Classifications of disabilities.Education data at district level Information about numbers of students and teachers by municipality and school type.Judicial committee register Information on violence against women,people moving home and divorce.Senior citizen identity card Individual information:name,age and address,etc.Smart Daughter Programme Information about family members:name,address and place of birth,etc.Table 3:Inventory of Tulsipurs official survey Data system What data is collected?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 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.All of the data sources included in the data inventory produce data disaggregated by gender.Therefore,we can conclude that gender disaggregation has been successfully mainstreamed into data collection activities in Tulsipur.LNOB Nepal:Data landscaping in Tulsipur municipality/devinit.org 6 Conversely,data disaggregated by geography(i.e.down to the ward or facility levels)is only collected by six of the data systems,and data disaggregated by age is only collected by five of the data systems(this does not include the education data available to us,as this was disaggregated by grade or year group).In addition to this,only three of the data sources produce data disaggregated by ethnicity.2 However,the lack of data disaggregated by ethnicity is not always due to this data not being collected.For example,administrative data is collected by ward offices when they issue disability identification cards.Initially,information is recorded on paper forms,and it is subsequently uploaded to an Excel file.Ethnicity data is collected on the paper forms,but it is routinely omitted from digital uploads.The paper forms then sit in storage and are not used.Stakeholders say the omissions are due to limited digital and human resource capacities.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.geographic location,age,ethnicity and disability)from 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 outcomes),and significantly limits the range of available evidence.Collecting and digitally storing disaggregated data across all data systems in Tulsipur would better enable intersectional analysis.LNOB Nepal:Data landscaping in Tulsipur municipality/devinit.org 7 Figure 1:There are low levels of disaggregated ethnicity and disability data in Tulsipurs 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 get to access this information in real time.3 However,data is collected and uploaded to other systems on a much more infrequent basis,meaning actors rely on information that may no longer be accurate or relevant.For example,data for the IEMIS is collected twice a year at the beginning and end of the academic calendar and is uploaded to the digital system by headteachers and the municipalitys education department around the same time it is collected.The schedule followed for the Prime Ministers employment program is similarly infrequent;data for it is only collected once a year(in February and March)and is uploaded to the digital system about a month later(in March and April).0123456789101112DisabilityEthnicityAgeWardGenderNumber of data systemsType of disaggregation LNOB Nepal:Data landscaping in Tulsipur municipality/devinit.org 8 Data is collected via the Municipal Profile Survey even less frequently:every five years.This infrequent scheduling is due to the costs of carrying it out.Therefore,despite the surveys capacity to produce good-quality statistics,it is not administered regularly enough to be relied on as the main source of evidence for local decision-makers.Data sharing and open data Data from the identified systems is generally not accessible in Tulsipur municipality;data from only three of the identified data systems is openly available,and in the case of two of these data systems,this is in the form of aggregated statistics,not microdata.Inaccessible data creates an obvious blockade preventing data use.Figure 2:The majority of data in Tulsipur is not openly accessible Proportions of open-access and inaccessible data in Tulsipur Source:DI,2023.However,there is reason to believe this will change in the future.Tulsipurs municipal government recently established an Integrated Data Management System(IDMS).The IDMS will pull data from other data systems semi-automatically,store it in a central database and display it on an open webpage.Presently,it is being piloted and some examples of anonymised data on civil registration,cooperatives,health,education,and agricultural services are available through it.While the implementation of the IDMS is good for data sharing and open data,it does raise concerns about privacy.This is because there is a risk that the individuals or Inaccessible data73%Open-access microdata9%Open-access aggregate statistics18%LNOB Nepal:Data landscaping in Tulsipur municipality/devinit.org 9 communities the published data is about will be identifiable.Nepals federal government has taken steps to make data protection more robust,namely through Article 29 of the Constitution(2015),the Individual Privacy Act(2075),the Muluki Criminal Code(2076),and the Individual Privacy Regulation(2077).However,the majority of staff in Tulsipur who will upload data to the IDMS are not aware of these laws.Ensuring staff are not only aware of these laws but also understand and abide by them is critically important.If this is done it will mean standards are kept,and it will reduce the risks associated with individuals relying on their own discretion to judge if something should or should not be published.This is especially important from a poverty and inequality perspective:if sensitive data about vulnerable individuals/communities is uploaded to the IDMS,it could put these people at a disproportionately increased risk of harm.Metadata Complete metadata allows actors to understand the data they are working with more promptly and thoroughly,and this encourages data use.Four of the 11 sources are accompanied by complete metadata;these are the VERSP-MIS,IEMIS,Health Management Information System and Employment Management Information System.A further four of the 11 data sources are accompanied by partially complete metadata.For example,the single Excel sheet where data for the senior citizen identity card,Smart Daughter Programme and disability identity card is collated,contains some metadata but is missing critical information such as the exact date the data was collected.LNOB Nepal:Data landscaping in Tulsipur municipality/devinit.org 10 Part 2:The use of poverty and inequality data in Tulsipur In general,the use of poverty and inequality data in Tulsipur is relatively low.For the most part,decisions are based on other priorities because actors have low levels of data literacy,do not fully appreciate the importance of evidence,perceive the quality of the available data as too poor to use,4 and/or make decisions to meet other objectives(such as maintaining patronage networks).Accessibility,particularly of microdata,is also a challenge that obstructs more data use.The culture of not sharing data is common across Nepal,and it even pervades the federal level of government:for example,the national statistical office does not currently have a policy for sharing or selling microdata,and,as such,it is not planning to share the latest census data with municipalities.However,the establishment of an Integrated Data Management System(IDMS)means that Tulsipur municipality is well placed to make improvements.Despite these challenges,a number of interviewees told us they are interested in developing their use of evidence.For example,the Deputy Mayor is planning to identify key indicators for measuring poverty in the municipality,which align with local needs,and will prepare an index based on them.However,the municipal government has not yet drafted a plan on how this will be achieved.Moreover,other participants at the project workshop suggested that increased data use could be encouraged by departments attempting to analyse existing data,identify problems with it(such as data gaps),and find solutions(e.g.additional indicators)that can be applied during upcoming data-collection cycles.LNOB Nepal:Data landscaping in Tulsipur municipality/devinit.org 11 Part 3:The foundations of Tulsipurs poverty and inequality data ecosystem Governance and