Introduction The COVID-19 pandemic has further highlighted the importance of having timely and reliable data that governments can use as inputs when designing and/or calibrating policy action plans. In a previous post, our team described how national statistical systems in the Asia–Pacific region have stepped up to provide timely data to policy makers and development practitioners. This new post elaborates on one of the strategies employed to fill gaps in work and employment-related statistics, which were conventionally compiled using face-to-face labor force surveys. Decent work and economic growth (Sustainable Development Goal No. 8) are among the global goals that make up the United Nations 2030 Agenda for Sustainable Development. SDG 8 pursues the realization of decent work for all men and women, productive and high-quality employment, and inclusive labor markets. The UN has defined 12 targets and 17 indicators for SDG 8, covering a wide range of labor-related topics. Labor statistics play a vital role in providing labor-related information, analyzing key trends, identifying potential strengths and weaknesses, as well as formulating policies, programs, and evaluating labor-related efforts. When the pandemic struck, governments imposed some of the most extensive community lockdowns in history, sharply constraining economic activity and upending livelihoods. Workers, particularly those who are in sectors that are not suitable for remote work, were severely affected with estimates suggesting that Asia and the Pacific lost as much as 8% of work hours in 2020. As unemployment rates soared, there was a need to enhance delivery of social protection programs for the most vulnerable workers. However, given that labor force surveys, which serve as an important source of work and employment-related data and statistics, were also hampered by restrictions in mobility, there was a need to harness alternative data sources. Prior to COVID-19, there had been increasing recognition about the rich potential that integration of multiple data sources offers in facilitating more efficient and effective development policy and program implementation. When the 2030 Sustainable Development Agenda was adopted in 2015, the need to revolutionize data collection, processing, and analysis became more apparent, to be able to meet enormous data requirements for monitoring the status of SDG targets. Censuses and sample surveys remain important sources of data for SDG indicators. Because of the high cost of collecting more granular and timely information through these data collection vehicles, the use of administrative data to compile statistical information has been gaining prominence among public and private researchers across the region. There are numerous and varied types of administrative records from which labor statistics may be compiled, including tax data, labor inspection records, employment service records, records of workers’ organizations, and social security and provident fund records. A Source of Rich Data In some countries and territories, government agencies are mandated to collect administrative data for statistical purposes. Administrative data, however, are generally derived from the administrative functions of an agency. As such, they are not designed to produce statistics, and their content, scope, coverage, and procedures may make them inappropriate for statistical use. Statisticians need to address these technical nuances to make the data more suitable for statistical analysis. Administrative records offer rich data that can play a significant part in the statistical data system, including labor statistics, making them a good alternative to address the data challenges of the national statistical systems. With administrative data, a complete count of units can be produced, and disaggregated data can be derived. Furthermore, utilizing already existing data will incur lower cost than designing a specialized data collection initiative to include the SDG indicators. Administrative records are typically the most timely and comprehensive source of statistics on social protection coverage and social security benefits (indicator 1.3.1 of SDG 1[1]). Indicator 8.8.1 of SDG 8, which assesses the incidence rate of fatal and nonfatal injuries, can be derived from various sources, including administrative records, such as insurance records, labor inspection records, and records kept by the labor ministry or the relevant social security institution. While data for indicator 8.5.1[2] are often derived from establishment surveys, administrative records, such as social security records, can provide more complete earnings data. Crises like the COVID-19 pandemic highlighted the importance of readily available data for policy makers and decision-makers to design well-informed interventions to aid the people. In the Philippines since the onset of COVID-19, the Department of Labor and Employment has been relying on administrative data, such as workers’ data registers, as references in its financial assistance program for displaced workers. The Department of Statistics Malaysia integrated administrative data from the Employees’ Provident Fund and the Inland Revenue Board to provide a snapshot on how the pandemic affected the labor market. The international statistical community also recognizes the importance of strengthening administrative data collection systems. The United Nations Statistics Division and the Global Partnership for Sustainable Development Data have jointly convened the Administrative Data Collaborative, which aims to strengthen the capacity of countries to use administrative records for statistical purposes. Opportunities for Data Integration The need for quality and granular data in the monitoring of labor market conditions and creating policies that are responsive to the needs of workers undoubtedly exist—much more so during crises. This need calls for existing labor-related data systems to be supplemented with other conventional data sources. A wide and growing scope of potential administrative sources is waiting to be tapped for this purpose. Administrative records pave the way for a range of opportunities for enhanced data and reporting of the SDGs. While challenges remain in the use of administrative data, they can be overcome with effective data management and better interagency coordination. Cooperation among local and international agencies plays a crucial role in setting high standards in data production through continued discussion of the best practices in the use of administrative data. The hiring and retention of highly qualified staff involved in data production is another key factor that should not be discounted. Creating a culture where data are highly regarded and used to formulate needed policies could encourage further investments in the use of administrative data. This will help build and sustain labor market data systems that are responsive to the needs of data users. Given the right skills, the availability of modern technologies also makes data integration more feasible and easier to manage. The integration of administrative data with traditional data sources will produce more high-quality, detailed, timely, and relevant information. In addition, this will potentially reduce the response burden of frequent surveys, help minimize the effects of declining response rates, and address unmet data needs, coverage, and bias in surveys. Consequently, data integration can help facilitate a more resilient national statistical system. [1] Proportion of population covered by social protection floors/systems, by sex, distinguishing children, unemployed persons, older persons, persons with disabilities, pregnant women, newborns, work-injury victims, and the poor and the vulnerable. [2] Average hourly earnings of employees, by sex, age, occupation, and persons with disabilities. Resources Asian Development Bank (ADB). 2021. Key Indicators for Asia and the Pacific 2021. Manila. ADB. 2021. Preparing a Road Map on the Use of Administrative Data for Compiling Employment Statistics. ADB Briefs. 179. Manila. V. Atisophon et al. 2021. How National Statistical Systems Provide Timely Data during the Pandemic. Development Asia. 13 September. Ask the Experts Christian Flora Mae Soco Consultant, Asian Development Bank Christian Flora Mae Soco is a consultant at the Economic Research and Regional Cooperation Department of the Asian Development Bank. Prior to joining ADB, she worked as a data analyst specializing in health claims data. She holds a degree in Statistics from the University of the Philippines Visayas. Remedios Baes-Espineda Consultant, Asian Development Bank Remedios Baes-Espineda is a consultant at the Economic Research and Regional Cooperation Department of the Asian Development Bank. Before joining ADB in 2013, she worked as a statistician at the Philippines' Bureau of Labor and Employment Statistics and National Statistics Office. Joseph Albert Nino M. Bulan Associate Statistics Analyst, Economic Research and Development Impact Department, Asian Development Bank Joseph Bulan serves as one of the focal persons for poverty and inequality. He is also part of the team compiling various indicators for the Key Indicators for the Asia and the Pacific report. He earned his Bachelor of Statistics in the University of the Philippines Los, Baños. Arturo Martinez, Jr. Senior Statistician, Economic Research and Development Impact Department, Asian Development Bank Art Martinez works on Sustainable Development Goals indicator compilation, particularly poverty statistics and big data analytics. Prior to joining ADB, he was a research fellow at the University of Queensland where he also got his doctorate in Social Statistics. Asian Development Bank (ADB) The Asian Development Bank is committed to achieving a prosperous, inclusive, resilient, and sustainable Asia and the Pacific, while sustaining its efforts to eradicate extreme poverty. Established in 1966, it is owned by 69 members—49 from the region. Its main instruments for helping its developing member countries are policy dialogue, loans, equity investments, guarantees, grants, and technical assistance. Follow Asian Development Bank (ADB) on Leave your question or comment in the section below: View the discussion thread.