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Published: 30 May 2022
Last updated: 31 May 2022

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1

Schneider, Joanne Thacker. Foster care: Fraught with data gaps and inadequate services. Sacramento, CA (Box 942849, Sacramento 94249-0001): Assembly Office of Research, 1989.

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2

Benaka, Lee R. Fisheries release mortality: Identifying, prioritizing, and resolving data gaps. Silver Spring, Maryland: U.S. Department of Commerce, National Oceanic and Atmospheric Administration, National Marine Fisheries Service, 2014.

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3

Fund, International Monetary. Price pressure gaps: An application of P* using Korean data. [Washington]: International Monetary Fund, European and Research Departments, 1991.

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4

Workshop on Nursing Data Gaps and Needs (1985 Washington, D.C.). Report of the workshop: Nursing Data Gaps & Needs, September 1985. [Rockville, MD]: The Division, 1986.

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5

Workshop on Nursing Data Gaps and Needs (1985 Washington, D.C.). Report of the workshop: Nursing Data Gaps & Needs, September 1985. [Rockville, MD]: The Division, 1986.

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6

Ahmad, Raza. Analysis of gaps in data-based interventions in reproductive health in Pakistan. Islamabad: Leads Pakistan, 2007.

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7

Hurst, Jill Ann. DialogWeb/FT: Navigating the periodical jungle : titles, variants, data gaps, and duplications. Woodstock, GA: Hermograph Press, 1999.

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8

Regional Community Forestry Training Center for Asia-Pacific (Bangkok, Thailand), ed. Forestry and poverty data in Viet Nam: Status, gaps, and potential uses. Bangkok, Thailand: Regional Community Forestry Training Center for Asia and the Pacific, 2009.

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9

Bower, Aimee. Equipment sustainment data in standard Army management information systems: Needs, gaps, and opportunities. Santa Monica, CA: RAND, 2012.

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10

Wahyunto. Peatland distribution in Sumatra and Kalimantan: Explanation of its data sets including source of information, accuracy, data constraints, and gaps. Bogor: Wetlands International, Indonesia Programme, 2008.

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11

Maltz, Michael D. Bridging gaps in police crime data: A discussion paper from the BJS fellows program. [Washington, DC]: U.S. Dept. of Justice, Office of Justice Programs, Bureau of Justice Statistics, 1999.

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12

Maltz, Michael D. Bridging gaps in police crime data: A discussion paper from the BJS fellows program. [Washington, DC]: U.S. Dept. of Justice, Office of Justice Programs, Bureau of Justice Statistics, 1999.

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13

Maltz, Michael D. Bridging gaps in police crime data: A discussion paper from the BJS fellows program. [Washington, DC]: U.S. Dept. of Justice, Office of Justice Programs, Bureau of Justice Statistics, 1999.

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14

Post, Roger A. Nonpoint-source pollution related to placer mining: Impacts, existing management, data gaps, and recommendations. Juneau, Alaska: The Department, 1988.

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15

Andrews, William J. Description of landscape features, summary of existing hydrologic data, and identification of data gaps for the Osage Nation, northeastern Oklahoma, 1890-2012. Reston, Virginia: U.S. Geological Survey, 2014.

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16

Folland, Chris. Representativeness, data gaps and uncertainties in climate observations: (invited scientific lecture given by Chris Folland to the Thirteenth WMO Congress, 21 May 1999). Geneva: World Meteorological Organization, 2000.

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17

Andrews, William J. Analysis of environmental setting, surface-water and groundwater data, and data gaps for the Citizen Potawatomi Nation Tribal Jurisdictional Area, Oklahoma, through 2011. Reston, Va: U.S. Dept. of the Interior, U.S. Geological Survey, 2013.

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18

United States. General Accounting Office. General Government Division. Immigration statistics: Information gaps, quality issues limit utility of federal data to policymakers : report to congressional requesters. Washington, D.C. (P.O. Box 37050, Washington, D.C. 20013): The Office, 1998.

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19

United States. General Accounting Office. General Government Division. Immigration statistics: Information gaps, quality issues limit utility of federal data to policymakers : report to congressional requesters. Washington, D.C: U.S. General Accounting Office, 1998.

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20

Wong, Carolyn. Using EPIC to find conflicts, inconsistencies, and gaps in Department of Defense policies. Santa Monica, CA: RAND National Defense Research Institute, 2013.

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21

Office, International Labour, ed. Gaps in basic workers' rights: Measuring international adherence to and implementation of the organization's values with public ILO data. Geneva: International Labour Office, 2003.

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22

Office, General Accounting. Year 2000: Insurance regulators have accelerated oversight, but some gaps remain : report to the Ranking Minority Member, Committee on Commerce, House of Representatives. Washington, D.C. (P.O. Box 37050, Washington, D.C. 20013): The Office, 1999.

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23

United States. Government Accountability Office. VA long-term care: Data gaps impede strategic planning for and oversight of state veterans' nursing homes : report to congressional committees. Washington, D.C: United States Government Accountability Office, 2006.

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24

Kass-Bartelmes, Barbara L. Disparities and gender gaps in women's health, 1996: Health insurance, access to care, health status. Rockville, MD: U.S. Dept. of Health and Human Services, Public Health Service, Agency for Healthcare Research and Quality, 2001.

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25

United States. Government Accountability Office. VA long-term care: Oversight of nursing home program impeded by data gaps : report to the Chairman, Committee on Veterans' Affairs, House of Representatives. Washington, D.C.]: The Office, 2004.

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26

Dodds, Dan. Natural gas data book. Olympia, WA (P.O. Box 43165, Olympia 98504-3165): Washington State Energy Office, 1992.

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27

Dodds, Daniel. Natural gas data book. Olympia, WA (809 Legion Way S.E., P.O. Box 43165, Olympia 98504-3165): Washington State Energy Office, 1992.

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28

Yaws, Carl L. Matheson gas data book. 7th ed. Parsippany, NJ: Matheson Tri-Gas, 2001.

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29

Association, Gas Processors. Engineering data book. Tulsa, Okla. (6526 E. 60th St., Tulsa 74145): Gas Processors Suppliers Association, 2004.

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30

Sing, Ashbindu. Spatial data sets for environmental assessment: Towards bridging the data gap. Nairobi, Kenya: Division of Environment Information and Assessment, United Nations Environment Programme, 1997.

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31

Singh, Ashbindu. Spatial data sets for environmental assessment: Towards bridging the data gap. [Nairobi]: UNEP/USGS, 1997.

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32

United States. Dept. of Transportation. and United States. Bureau of Transportation Statistics., eds. Bicycle and pedestrian data: Sources, needs, and gaps. Washington, D.C: U.S. Dept. of Transportation, Bureau of Transportation Statistics, 2000.

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33

United States. Bureau of Justice Statistics, ed. Bridging Gaps in Police Crime Data, Correction Notice. [S.l: s.n., 1999.

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34

United States. Bureau of Justice Statistics, ed. Corrections: Bridging Gaps In Police Crime Data, April 2000. [S.l: s.n., 2000.

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35

United States. Bureau of Transportation Statistics, ed. Bicycle And Pedestrian Data: Sources, Needs And Gaps, 2000. [S.l: s.n., 2000.

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36

Berkan, Louise O. U. S. Weather Satellites: Background, Program Challenges and Potential Data Gaps. Nova Science Publishers, Incorporated, 2015.

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37

Larson, Eric M. Immigration Statistics: Information Gaps, Quality Issues Limit Utility of Federal Data to Policymakers. Diane Pub Co, 1998.

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38

Filling the Gaps: An Overview of Data on Education in Grades K Thru 12. United States Government Printing, 1992.

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39

Morrison, Judith, and Marcos Robles. Recommendations on Diversity Data in the Social Sector. Inter-American Development Bank, 2021. http://dx.doi.org/10.18235/0003856.

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Administrative records, surveys, and censuses are all important resources for understanding the extent and nature of existing inequities and biases. When individuals, households, and peoples (pueblos) are properly identified based on their ethnicity, race, disability status, sexual orientation, migration status, gender identity, or other characteristics, gaps in socioeconomic indicators across these groups can be quantified. This enables policymakers to focus efforts and resources towards the most disadvantaged and promote equity. The lack of disaggregated data representative of diverse groups limits our understanding of their living conditions and economic opportunities. It also poses a significant challenge. This guide supports individuals interested in collecting or analyzing data on indigenous peoples, African descendants, people with disabilities, and LGBTQ populations by providing conceptual and empirical tools for specialists.
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40

Morrison, Judith, and Marcos Robles. Recommendations on Diversity Data in the Social Sector. Inter-American Development Bank, 2021. http://dx.doi.org/10.18235/000385.

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Administrative records, surveys, and censuses are all important resources for understanding the extent and nature of existing inequities and biases. When individuals, households, and peoples (pueblos) are properly identified based on their ethnicity, race, disability status, sexual orientation, migration status, gender identity, or other characteristics, gaps in socioeconomic indicators across these groups can be quantified. This enables policymakers to focus efforts and resources towards the most disadvantaged and promote equity. The lack of disaggregated data representative of diverse groups limits our understanding of their living conditions and economic opportunities. It also poses a significant challenge. This guide supports individuals interested in collecting or analyzing data on indigenous peoples, African descendants, people with disabilities, and LGBTQ populations by providing conceptual and empirical tools for specialists.
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41

Proceedings of the national seminar on gender statistics and data gaps, Goa 5 - 7th Febrary 2004. New Delhi: Central Statistical Organisation, Ministry of Statistics and Programme Implementation, 2009.

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42

California. Legislature. Senate. Office of Research. and California. Legislature. Senate. Select Committee on Children and Youth., eds. Children's services in California: Data trends, gaps, and sources : a special report to Senator Robert Presley. Sacramento, CA: The Office, 1989.

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43

Kareiva, Peter, and Michelle Marvier. Uncomfortable questions and inconvenient data in conservation science. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198808978.003.0001.

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This chapter outlines the problems of exaggeration, misuse of statistics, and publication bias that plague all scientific disciplines, but that may be especially acute in the mission-oriented field of conservation. Because conservation describes itself as a crisis discipline, its scientific publications tend to reinforce that view, even when the data are lacking. And when data run counter to accepted wisdom, out of fear such results might be misinterpreted or misused to counter conservation’s mission, the review process sometimes favors dogma over data. Black-box models, data gaps filled with expert opinion, and a general lack of easy access to key data make the testing of alternative hypotheses or interpretations extremely difficult. Self-correction and iteration are key to scientific progress. In conservation especially, with the fate of biodiversity in the balance, it is essential that conservationists get the science right, even if it means admitting mistakes.
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44

Immigration statistics: Information gaps, quality issues limit utility of federal data to policymakers : report to congressional requesters. Washington, D.C. (P.O. Box 37050, Washington, D.C. 20013): The Office, 1998.

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45

Egger, Eva-Maria, Aslihan Arslan, and Emanuele Zucchini. Does connectivity reduce gender gaps in off-farm employment? Evidence from 12 low- and middle-income countries. 3rd ed. UNU-WIDER, 2021. http://dx.doi.org/10.35188/unu-wider/2021/937-2.

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Gender gaps in labour force participation in developing countries persist despite income growth or structural change. We assess this persistence across economic geographies within countries, focusing on youth employment in off-farm wage jobs. We combine household survey data from 12 low- and middle-income countries in Asia, Latin America, and sub-Saharan Africa with geospatial data on population density, and estimate simultaneous probit models of different activity choices across the rural-urban gradient. The gender gap increases with connectivity from rural to peri-urban areas, and disappears in high-density urban areas. In non-rural areas, child dependency does not constrain young women, and secondary education improves their access to off-farm employment. The gender gap persists for married young women independent of connectivity improvements, indicating social norm constraints. Marital status and child dependency are associated positively with male participation, and negatively with female participation; other factors such as education are show a positive association for both sexes. These results indicate entry points for policy.
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46

Hoover Green, Amelia. “Mind the Gap”. Edited by Fionnuala Ní Aoláin, Naomi Cahn, Dina Francesca Haynes, and Nahla Valji. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199300983.013.25.

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This chapter outlines two key methodological gaps in conflict data analysis: (1) the gap between collected data and underlying reality; and (2) the gap between measurements of specific experiences and the broader reality of the conflict. With respect to the first gap, the chapter reviews potential shortcomings in various forms of data collection including data from surveys, media sources, health sector sources, legal mechanisms, and nongovernmental organization (NGO) case files. With respect to the second gap, the chapter describes three common issues in translating measurements of specific experiences into broader knowledge about conflict: misunderstanding the gendered nature of violence; making incorrect inferences of subjective experiences of violence; and overlooking potential gendered benefits of conflict. The chapter offers recommendations for researchers and suggests a more careful acknowledgment of the shortcomings of data when presenting findings.
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47

Atamanov, Aziz, Sharad Tandon, Gladys Lopez-Acevedo, and Mexico Alberto Vergara Bahena. Measuring Monetary Poverty in the Middle East and North Africa (MENA) Region: Data Gaps and Different Options to Address Them. World Bank, Washington, DC, 2020. http://dx.doi.org/10.1596/1813-9450-9259.

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48

Hinojosa, Magda, and Miki Caul Kittilson. Seeing Women, Strengthening Democracy. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780197526941.001.0001.

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How does the more equitable representation of women in positions of power affect male and female citizens? We argue that the election of women to political office—particularly where women’s presence is highly visible to the public—strengthens the connections between women and the democratic process. For women, seeing more “people like me” in politics changes attitudes and orientations toward the democratic process. Substantial variation persists across Latin America in gender gaps in political engagement and political support. To assess the effects that women’s officeholding has on these, we pair comparative survey data from Latin American countries with case study evidence from Uruguay. The Uruguayan case offers a unique laboratory for testing the impact of women’s representation in elected positions of power on political engagement and support. Our panel survey of Uruguayan citizens reveals that the expected gender gaps in political knowledge, political interest, and other forms of political engagement were alive and well six weeks before the elections. Yet, just six weeks following the election—after the use of a gender quota had led to a doubling of women’s representation in the Senate—those gender gaps had largely disappeared or had significantly waned. Our findings indicate that far-reaching gender gaps can be overcome by more equitable representation in our political institutions.
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49

Health of Afro-descendant People in Latin America. Pan American Health Organization, 2022. http://dx.doi.org/10.37774/9789275124895.

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This report will build upon previous work and reports, analyzing existing publications to consolidate data already available on Afrodescendent health, with the aim of identifying key gaps in data on Afrodescendent health in the region, and recommending the areas of need for further work. This Regional Report on Afrodescendent Health in the Americas aims to analyze various dimensions of Afrodescent health, with particular attention to access to health service and social protection for these populations.
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50

Martin, Philip. Migration Costs of Returning Workers. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198808022.003.0005.

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This chapter analyzes data on what workers—interviewed as they returned from jobs in Gulf Cooperation Council countries—paid several years earlier at home to get their jobs. The most important finding is the big difference in cost by corridor. The gap between the highest and lowest cost corridor was 9 to 1, reflecting the average $4,400 paid by Pakistanis returning from Saudia Arabia versus the $500 paid by Filipinos returning from Qatar. Earnings gaps were much smaller, ranging from a low of $350 a month for Ethiopians returning from Saudi Arabia to $600 for Indians returning from Qatar. The fact that costs vary much more than earnings suggests that differing conditions in migrant-sending countries might explain why some workers pay much more than others.
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