“編制利用行政數(shù)據(jù)編制就業(yè)統(tǒng)計(jì)的路線圖

1、ADB BRIEFSNO. 179JUNE 2021KEY POINTSIntegration of data from multiple sources can provide more nuanced and meaningful informationto meet the enormous data requirements of the Sustainable Development Goals (SDGs) more efficiently.The need to improve the efficiency of the statistical production proces

2、s prompts official statisticians to explore alternatives to traditional survey-based approach of collecting data. Administrative data sources, when used properly, can complement survey data by providing more timely and comprehensive information.Compilation of work and employment statistics is an are

3、a that can significantly benefit from integrating administrative data. In Asia and the Pacific, how administrative sources are used to produce work- related statistics varies considerablyfurther magnifying the need for statistical capacity building in this area, especially theability of statistical

4、systems to bear, recover from, and respond and/or adapt to external disturbance.Preparing a Road Map on the Use of Administrative Data for Compiling Employment StatisticsINTRODUCTIONPeople increasingly recognize that having high-quality data as inputs to guide governments and policy makers is import

5、ant to ensure that program implementation can yield highly beneficial outputs, outcomes, and impact for societies. Data-driven policy-making and monitoring also helps invigorate national and international developmental efforts. However, the budget constraints experienced by many national statistical

6、 offices (NSOs) in recent years have made it even more challenging to meet the increasing demand to produce high-quality data. Consequently, NSOs need tobe more resourceful in producing such outputs, including maximizing the use of all available data.1The importance of high-quality data was further

7、magnified with the global adoption of the Millennium Development Agenda in 2000, and the adoption in 2015 of the2030 Agenda for Sustainable Development, which has four times as many indicators as its predecessor. Progress on the Millennium Development Goals (MDGs) and the SDGs have been monitored us

8、ing corresponding indicators for each goal and target.It is imperative that the data used in these indicators are of good quality as they can help shape essential policies.Countries gradually moved from setting their development priorities independently to integrating their national development stra

9、tegies with the global development agenda. National statistical systems were prompted to strengthen their capacity and methodologies and ensure that they adhere to international statistical standards for compiling development data. This further cemented the important role of data in socioeconomic de

10、velopment and underlined development challenges such as lack of reliable and internationally comparable data that can undermine governments ability to set goals, optimize investments decisions, and measure progress.ISBN 978-92-9262-896-3 (print)ISBN 978-92-9262-897-0 (electronic)ISSN 2071-7202 (prin

11、t)ISSN 2218-2675 (electronic) Publication Stock No. BRF210195-2DOI: HYPERLINK /10.22617/BRF210195-2 /10.22617/BRF210195-21This brief forms part of the outputs of TA 9646: Data for Development (Phase 2). The brief was prepared by Christian Flora Mae Soco, Remedios Baes-Espineda, Arturo Martinez Jr.,

12、and Joseph Bulan from the Statistics and Data Innovation Unit, Economic Research and Regional Cooperation Department. Technical inputs were provided by consultants Marymell Martillan, Mildred Addawe, and Jose Ramon Albert.As policy makers need for data expands, there is a need to revolutionize data

13、collection, processing, and usage by capitalizing on different data sources and technological developments. To do so, it is important to understand the many ways in which data are being collected, compiled, analyzed, and disseminated. The four common sources of data for development are censuses, sam

14、ple surveys, big data, and administrative data.Censuses. By conducting a complete enumeration of all units in the population, a census provides reliable baseline data on the structure and key characteristics of the target population against which changes through time can be measured. However, given

15、their scope and range, censuses entail high costs and robust staffing.Furthermore, the large number of respondents and differences in understanding of concepts, definitions, and instructions predispose censuses to data collection errors. As with other data sources, changes in census methodologies ca

16、n also make it challenging to compare data from one census to another.Sample surveys. Sample surveys, on the other hand, collect data from a fraction of the population and aim to draw inferences about the entire population. They are a more cost-effective means of collecting data where a complete ass

17、essment of the whole population is not required. Because they are administered in a more controlled manner, sample surveys can include detailed inquiries with multiple questions on the characteristics of the target population that are of interest. They also minimize non- sampling errors. However, as

18、 much as sample surveys generate data in a more cost-effective manner, they also bear the trade-off for a good sampling design and the required sample size which usually result to sampling errors. The success of a sample survey depends on the percentage of response and the quality of theresponses, i

19、ncluding the respondents ability to recall, their honesty, and their motivation to respond to the set of questions.As with censuses, comparability over time is also a challenge because estimates of key variables may require similar designs and methods that are highly unlikely to be perfectly replica

20、ted. Furthermore, there is a need for adequately trained staff to administer the survey with minimal deviation from the standards.Big data. As modernization in information and communication technology (ICT) resulted in a data revolution, the world gets to experience an upsurge in data capture, produ

21、ction, storage, access, analysis, archive, and reanalysis more than ever. Though access to and storage of large volume of data for analytics have existed for quite a while, the concept of big data started to gain popularityin the early 2000s. Big data is the “information asset characterized by such

22、a high volume, velocity and variety to require specific technology and analytical methods for its transformationinto value.”2The coronavirus disease (COVID-19) pandemic, for instance, magnified the significance of available and timely data as the backbone of well-informed interventions to aid people

23、 affected by the crisis.In the context of the 2030 Sustainable Development Agenda that no one will be left behind, there is an apparent need to enhance current statistical data collection system if the national statistical offices are to use big data as a data source. Among others, the current hardw

24、are and software systems in place in the NSOs definitely require an upgrading. As big data comes in various formats, national statisticians also need to be reinforced in terms of their capacity to analyze unstructured, semi-structured, and structured data.Administrative data. Several MDG and SDG ind

25、icators have used administrative data as a main or supplementary source of information. Administrative data are also widely used in setting up other data systems such as firm registration, health records, school enrollment, customs data, and tax records. The use of administrative data has several be

26、nefits. For instance, a complete count of units can be produced and disaggregated data fromsmaller areas of interests can be derived. In addition, using existing data incurs lower costs than designing a specialized data collection initiative that is solely intended to serve specific data needs.Indir

27、ectly, the use of administrative data to collect information that is generally sourced from surveys and censuses can also help reduce respondent burden and consequently minimize nonresponse bias.The importance of readily available data from administrative registers is further highlighted during cris

28、es. The coronavirus disease (COVID-19) pandemic, for instance, magnified the significance of available and timely data as the backbone of well-informed interventions to aid people affected by the crisis. During this period, several countries have turned to information available from administrative d

29、ata to design interventions.For example, since the onset of COVID-19, there are examples on the use of administrative data, such as workers registers or records, as references for its financial assistance program for displaced workers.2 A. De Mauro, M. Greco, and M. Grimaldi. 2016. A Formal Definiti

30、on of Big Data Based on its Essential Features. HYPERLINK /publication/299379163_A_formal_definition_of_Big_Data_based_on_its_essential_features / HYPERLINK /publication/299379163_A_formal_definition_of_Big_Data_based_on_its_essential_features publication/299379163_A_formal_definition_of_Big_Data_ba

31、sed_on_its_essential_features.Within the international statistical community, there is also an increasing recognition of the importance of strengtheningadministrative data collection systems. For instance, Collaborative3 is an initiative established to strengthen the use of administrative records fo

32、r statistical purposes that aims to address the need for timely and disaggregated statistics to mitigate the impact of the pandemic. This partnership is supported by NSOs and regional and international agencies and co-convened by the United Nations (UN) Statistics Division and the Global Partnership

33、 for Sustainable Development Data.In 2010, the Asian Development Bank published a handbook on the use of administrative data sources for compiling MDG and related indicators, focusing on education, health, and registration systems, and featuring practices and experiences from selected countries. Thi

34、s brief supplements the handbook by providing additional insights on important elements of developing a road map on how national statistical systems can capitalize on administrative data sources to monitor the SDGs, particularly indicators relatedto the labor market and employment. These types of de

35、velopment indicator make for an interesting case study because labor statistics derived from administrative data, such as employment registration or labor inspection records, can provide information to formulate and evaluate action plans on unemployment and work conditions, and to gauge the prevalen

36、ce of the working poor.Understanding how countries use administrative data to compile work- and employment-related indicators to design labor policies, including its associated limitations and challenges with or without crisis, can help address the increasing demand for information and insights. Suc

37、h demand compels statistical agencies to look for alternative data sources. It is well-recognized that quickly modifying existing traditional data sources such as surveys by adding new questions to produce new sets of data requiresenormous government resources while also adding to respondents burden

38、. Building new traditional data sources is even morecostly. Exploring the use of administrative data promises many advantages, including cost efficiency, as merging these sets of data can produce rich new data sources. Furthermore, it can provide important lessons for existing users and others who h

39、ave yet to capitalize on administrative data.SUSTAINABLE DEVELOPMENT GOAL LABOR INDICATORSDealing with work, productive activities, and workers and their characteristics, labor statistics encompass and reflect the attributes of the labor market and its operations. The scope of labor statistics is va

40、st and includes both the supply of and demand for labor. International standards must be followed to ensure the comparability of labor statistics. Similarly, the quality of these statisticsanchored in the methodology, including the strengthsExploring the use of administrative data promises countless

41、 advantages, including cost efficiency, as merging these sets of data can produce rich new data sources.and limitations of the data source must also be guaranteed. At the core of inclusive economic growth and development are labor markets providing decent and dignified opportunities for people withi

42、n countries and across borders.Labor statistics help decision makers engage with and better understand common labor market problems and devise and promote actions to address them. They also play a vital role in obtaining labor-related information, analyzing key trends, identifying potential strength

43、s and weaknesses, formulating policies and programs, and evaluating labor-related efforts.Their ability to monitor labor at the macroeconomic level is key to achieving SDG 8 of the UN 2030 Agendadecent work for all.SDG 8 pursues the realization of decent work for all men and women; productive, high-

44、quality employment; and inclusive labor markets. It cuts across other goals and is connected with many targets in the agenda. The UN has defined 12 targets and 17 indicators for SDG 8 covering a wide range of labor-related topics. Most SDG labor market indicators relate to SDG 8, but some refer to o

45、ther goals, such as SDG 1 (End poverty), SDG 4(Ensure quality education), SDG 5 (Achieve gender equality), SDG 10 (Reduce inequality), SDG 14 (Conserve marine resources), and SDG 16 (Promote justice and institutions).The International Labour Organization (ILO) is either the sole custodian agency, on

46、e of the custodian agencies, or a partner agency for the 17 SDG labor market indicators. Table 1 presents the 17 indicators as well as an indicator that is still under discussion.The United Nations Statistical Commission established theInter-agency and Expert Group on SDG Indicators to develop and i

47、mplement an indicator framework for the SDGs and targets of the 2030 Agenda. Indicators have a tiered classification based on the level of methodological development and the availability of data at the global level. Tier I indicators are those that are conceptually clear, have internationally establ

48、ished methodologies, standards are available, and data are regularly produced by countries.Tier II indicators are those that are conceptually clear, have internationally established methodologies, standards are available, but data are not regularly produced by countries. In the latest update on 29 M

49、arch 2021, 6 labor-related SDG indicatorsare classified under Tier I and 12 are classified as Tier II.3 United Nations Statistics Division. Using administrative data to help inform COVID-19 response. HYPERLINK /capacity-development/meetings/Using-administrative-data-to-COVID-19-response/ /capacity-d

50、evelopment/meetings/Using- HYPERLINK /capacity-development/meetings/Using-administrative-data-to-COVID-19-response/ administrative-data-to-COVID-19-response/.Table 1: Sustainable Development Goal Labor IndicatorsIndicator NumberIndicator TitleCustodian Agency(ies)Partner Agency(ies)Tier Classificati

51、onData SourcesTypes of Household Surveys1.1.1Proportion of the population living below the international poverty line by sex, age, employment status and geographic location (urban/rural)World BankInternational Labour Organization (ILO)Tier IHousehold surveysHousehold income and expenditure surveys,

52、labor force surveys (LFSs), demographic and health surveys, living standards measurement studies, household budget surveys, living standards measurement studies (LSMS) with employment modules, or LFS with information onhousehold income1.3.1Proportion of population covered by social protection floors

53、/systems, by sex, distinguishing children, unemployed persons,older persons, persons with disabilities, pregnant women, newborns,work-injury victims, and thepoor and the vulnerableILOWorld BankTier IIAdministrative data; household surveys (partially)1.a.2Proportion of total government spending on es

54、sential services (education, health, and social protection)Under discussion among agencies (ILO, United Nations Educational,Scientific and Cultural Organization Institute forStatistics UIS, World Health OrganizationWHO)Tier II4.3.1Participation rate of youth and adults in formal and non-formal educa

55、tion and training in the previous12 months, by sexUNESCO-UISOrganisation for Economic Co- operation and Development,Eurostat, ILOTier IIHousehold surveysLFS or LSMS5.5.2Proportion of women in managerial positionsILOWorld Bank, United Nations Statistics DivisionTier IHousehold surveys; administrative

56、 data(alternative)LFS or other types of household surveys with module on employment8.2.1Annual growth rate of real gross domestic product per employed personILOWorld Bank, United Nations StatisticsDivisionTier IHousehold surveysLFScontinued on next pageTable 1 continuedIndicator NumberIndicator Titl

57、eCustodian Agency(ies)Partner Agency(ies)Tier ClassificationData SourcesTypes of Household Surveys8.3.1Proportion of informal employment in total employment, by sector andsexILOTier IIHousehold surveysLFS or LSMS8.5.1Average hourly earnings of employees, by sex, age, occupation, and personswith disa

58、bilitiesILOTier IIHousehold surveys; establishmentsurveys8.5.2Unemployment rate,by sex, age, and persons with disabilitiesILOTier IHousehold surveysLFS, LSMS, or other household surveys with information on employment andunemployment8.6.1Proportion of youth (aged 1524 years) not in education, employm

59、ent, or trainingILOTier IHousehold surveysLFS, LSMS, or other household survey with information on employment andunemployment8.7.1Proportion and number of children aged 517 years engaged in child labor,by sex and ageILO, United Nations Childrens Fund (UNICEF)Tier IIHousehold surveysMultiple indicato

60、r cluster surveys, demographic and health surveys, child laborsurvey, LFS, or LSMS8.8.1Fatal and nonfatal occupational injuries per 100,000 workers, by sex and migrant statusILOTier IIAdministrative data; household surveys(partially)Household surveys covering informal sector enterprises8.8.2Level of