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Suggested Citation:"References." National Academies of Sciences, Engineering, and Medicine. 2017. Innovations in Federal Statistics: Combining Data Sources While Protecting Privacy. Washington, DC: The National Academies Press. doi: 10.17226/24652.
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References

Abowd, J. (2016a). How Will Statistical Agencies Operate When All Data Are Private? Available: http://digitalcommons.ilr.cornell.edu/ldi/30 [December 2016].

Abowd, J. (2016b). Why Statistical Agencies Need to Take Privacy-Loss Budgets Seriously, and What It Means When They Do. Proceedings of the Federal Committee on Statistical Methodology Policy Seminar, Washington, DC, December 7. Available: http://digitalcommons.ilr.cornell.edu/ldi/32 [December 2016].

Abowd, J., and Schmutte, I. (2016). Revisiting the Economics of Privacy: Population Statistics and Confidentiality Protection as Public Goods. Available: http://digitalcommons.ilr.cornell.edu/ldi/22 [December 2016].

Abowd, J., and Vilhuber, L. (2005). The sensitivity of economic statistics to coding errors in personal identifiers. Journal of Business and Economics Statistics, 23(2), 133-152.

Abowd, J., Haltiwanger, J., and Lane, J. (2004). Integrated longitudinal employer-employee data for the United States. American Economic Review Papers and Proceedings, 94(2), 224-229.

Administrative Data Research Network. (2015). Better Knowledge Better Society: Network Review. Available: https://adrn.ac.uk/media/1193/adrn-annual-review-2014-2015_web.pdf [November 2016].

Advisory Commission to Study the Consumer Price Index. (1996). Toward a More Accurate Measure of the Cost of Living. Available: https://www.ssa.gov/history/reports/boskinrpt.html [November 2016].

Agre, P.E., and Rotenberg, M. (1998). Technology and Privacy: The New Landscape. Cambridge, MA: The MIT Press.

Ahas, R., Tiru, M., Saluveer, E., and Demunter, C. (2011). Mobile Telephones and Mobile Positioning Data as Source for Statistics: Estonian Experiences. Available: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.461.3362&rep=rep1&type=pdf [November 2016].

Allen, A.L., and Rotenberg, M. (2016). Privacy Law and Society (3rd Edition). St. Paul, MN: West Academic Publishing.

Suggested Citation:"References." National Academies of Sciences, Engineering, and Medicine. 2017. Innovations in Federal Statistics: Combining Data Sources While Protecting Privacy. Washington, DC: The National Academies Press. doi: 10.17226/24652.
×

Australian Bureau of Statistics. (2015). Report from the Task Team on Satellite Imagery, Remote Sensing and Geospatial Data. Available: http://unstats.un.org/unsd/trade/events/2015/abudhabi/presentations/day2/01/1%20ABu%20Dhabi%20-%20Introduction%20and%20overview%20-%2020%20October%20version.pdf [November 2016].

Baker, R., Blumberg, S., Brick, J.M., Couper, M., Courtright, M., Dennis, J.M., Dillman, D., Frankel, M., Garland, P., Groves, R., Kennedy, C., Krosnick, J., and Lavrakas, P. (2010). AAPOR report on online panels. Public Opinion Quarterly, 1-71. Available: https://pprg.stanford.edu/wp-content/uploads/2010-AAPOR-Report-on-Online-Panels.pdf [November 2016].

Becker, A. (2015). Ninety-One Percent of Colleges Report Zero Rapes in 2014. Available: http://www.aauw.org/article/clery-act-data-analysis [November 2016].

Bellhouse, D.R. (2000). Survey sampling theory over the twentieth century and its relation to computing technology. Survey Methodology, 26, 11-20.

Bender, S., Jarmin, R., Kreuter, F., and Lane, J. (2016). Privacy and confidentiality. In I. Foster, R. Ghani, R.S. Jarmin, F. Kreuter, and J. Lane (Eds.), Big Data and Social Science: A Practical Guide to Methods and Tools (pp. 299-312). New York: CRC Press.

Benedetto, G., Stinson, M., and Abowd, J.M. (2013). The Creation and Use of the SIPP Synthetic Beta. Suitland, MD: U.S Census Bureau. Available: https://www.census.gov/content/dam/Census/programs-surveys/sipp/methodology/SSBdescribe_nontechnical.pdf [November 2016].

Beuken, Y., and Vlag, P. (2010). Business Register: The Dutch Experience. Available: https://www.ine.pt/filme_inst/essnet/papers/Session4/Paper4.3.pdf [November 2016].

Blumberg, S.J., and Luke, J.V. (2007). Coverage bias in traditional telephone surveys of low-income and young adults. Public Opinion Quarterly, 71(5), 734-749.

Blumberg, S.J., and Luke, J.V. (2011). Wireless Substitution: Early Release of Estimates from the National Health Interview Survey, January-June 2011. Available: http://www.cdc.gov/nchs/data/nhis/earlyrelease/wireless201112.pdf [November 2016].

Blumberg, S.J., and Luke, J.V. (2016). Wireless Substitution: Early Release of Estimates from the National Health Interview Survey, July-December 2015. Available: http://www.cdc.gov/nchs/data/nhis/earlyrelease/wireless201605.pdf [November 2016].

Blumerman, L.M., and Vidal, P.M. (2009). Uses of Population and Income Statistics in Federal Funds Distribution—With a Focus on Census Bureau Data. Governments Division Report Series, Research Reports No. 2009-1. Available: https://www.census.gov/prod/2009pubs/govsrr2009-1.pdf [November 2016].

Boneh, D., Sahai, A., and Waters, B. (2011). Functional Encryption: Definitions and Challenges. Proceedings of the Theory of Cryptography Conference (TCC) 2011, Baltimore, MD, November 13-15. Available: https://eprint.iacr.org/2010/543 [November 2016].

Brick, J.M., and Williams, D. (2013). Explaining rising nonresponse rates in cross-sectional surveys. The ANNALS of the American Academy of Political and Social Science, 645(1), 36-59.

Bureau of the Census. (1975). Historical Statistics of the United States: Colonial Times to 1970. Available: https://www.census.gov/history/pdf/histstats-colonial-1970.pdf [November 2016].

Bureau of Justice Statistics. (2003). Level of UCR and NIBRS Participation. Available: http://www.bjs.gov/content/nibrsstatus.cfm [November 2016].

Bureau of Labor Statistics and U.S. Census Bureau. (2006). Design and Methodology: Current Population Survey. Technical Paper 66. Available: http://www.census.gov/prod/2006pubs/tp-66.pdf [November 2016].

Calandrino, J.A., Kilzer, A., Narayanan, A., Felten, E., and Shmatikov, V. (2011). “You Might Also Like”: Privacy Risks of Collaborative Filtering. Available: https://www.cs.utexas.edu/~shmat/shmat_oak11ymal.pdf [November 2016].

Suggested Citation:"References." National Academies of Sciences, Engineering, and Medicine. 2017. Innovations in Federal Statistics: Combining Data Sources While Protecting Privacy. Washington, DC: The National Academies Press. doi: 10.17226/24652.
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California State Auditor. (2015). California Post Secondary Educational Institutions: More Guidance Is Needed to Increase Compliance With Federal Crime Reporting Requirements. Report 2015-032. Available: http://auditor.ca.gov/pdfs/reports/2015-032.pdf [November 2016].

Card, D., Chetty, R., Feldstein, M., and Saez, E. (2010). Expanding Access to Administrative Data for Research in the United States. Arlington, VA: National Science Foundation. Available: www.nsf.gov/sbe/sbe_2020/submission_detail.cfm?upld_id=112 [November 2016].

Catalano, S.M. (2004). Crime Victimization, 2003. Bureau of Justice Statistics, NCJ 205455. Available: https://www.bjs.gov/content/pub/pdf/cv04.pdf [November 2016].

Cavallo, A., and Rigobon, R. (2016). The Billion Prices Project: Using Online Prices for Measurement and Research. Working Paper 22111. Cambridge, MA: National Bureau of Economic Research. Available: http://www.nber.org/papers/w22111 [November 2016].

Centers for Disease Control and Prevention. (2015). National Immunization Survey: A User’s Guide for the 2014 Public-Use Data File. Available: ftp://ftp.cdc.gov/pub/Health_Statistics/NCHS/Dataset_Documentation/NIS/NISPUF14_DUG.pdf [November 2016].

Chen, X., and Nordhaus, W. (2010). The Value of Luminosity Data as a Proxy for Economic Statistics. Working Paper 16317. Cambridge, MA: National Bureau of Economic Research. Available: http://www.nber.org/papers/w16317 [November 2016].

Chessa, A.G. (2016). Processing Scanner Data in the Dutch CPI: A New Methodology and First Experiences. Proceedings of the Meeting of the Group of Experts on Consumer Price Indices, Geneva, Switzerland, May 2-4. Available: https://www.unece.org/fileadmin/DAM/stats/documents/ece/ces/ge.22/2016/Session_1._Netherlands_Processing_scanner_data_in_the_Dutch_CPI.pdf [December 2016].

Chirgwin, R. (2016). The Australian Bureau of Statistics Has Made a Hash of the Census. Available: http://www.theregister.co.uk/2016/08/01/the_abs_has_burned_trust_and_thats_a_problem [November 2016].

Citro, C. (2014). From multiple modes for surveys to multiple data sources for estimates. Survey Methodology, 40(2), 137-161.

Cochran, W.G. (1953). Sampling Techniques. New York: Wiley; London: Chapman & Hall.

Cohany, S.R., Polivka, A.E., and Rothgeb, J.M. (1994). Revisions in the Current Population Survey effective January 1994. In Employment and Earnings (pp. 13-37). Washington, DC: Bureau of Labor Statistics. Available: http://www.bls.gov/cps/revisions1994.pdf [November 2016].

Colombia National Statistics Office. (2016). Use of Satellite Images to Calculate Statistics on Land Cover and Land Use. Available: http://cepei.org/wp-content/uploads/2016/08/report-pilot-project-colombia-v3.pdf [November 2016].

Comey, J.B. (2015). Hard Truths: Law Enforcement and Race. Available: https://www.fbi.gov/news/speeches/hard-truths-law-enforcement-and-race [November 2016].

Couper, M. (2013). Is the sky falling? New technology, changing media, and the future of surveys. Survey Research Methods, 7(3), 145-156.

Cruze, N.B. (2015). Integrating survey data with auxiliary sources of information to estimate crop yields. In Proceedings of the Survey Research Methods Section (pp. 565-578). Washington, DC: American Statistical Association.

Czajka, J., and Beyler, A. (2016). Declining Response Rates in Federal Surveys: Trends and Implications. Washington, DC: Mathematica Policy Research.

Czajka, J.L., and Denmead, G. (2008). Income Data for Policy Analysis: A Comparative Assessment of Eight Surveys. Washington, DC: Mathematica Policy Research.

Daas, P., and Ossen, S. (2011). Metadata quality evaluation of secondary data sources. International Journal for Quality Research, 5(2), 57-66.

Suggested Citation:"References." National Academies of Sciences, Engineering, and Medicine. 2017. Innovations in Federal Statistics: Combining Data Sources While Protecting Privacy. Washington, DC: The National Academies Press. doi: 10.17226/24652.
×

Daas, P., and Puts, M. (2014). Social Media Sentiment and Consumer Confidence. European Central Bank and Statistics Paper Series No. 5. Available: http://www.pietdaas.nl/beta/pubs/pubs/Ecbsp5.pdf [November 2016].

Daas, P.J.H., Puts, M.J., Buelens, B., and van den Hurk, P.A.M. (2015). Big data as a source for official statistics. Journal of Official Statistics, 31(2), 249-262.

Daries, J.P., Reich, J., Waldo, J., Young, E.M., Whittinghill, J., Ho, A.D., Seaton, D.T., and Chuang, I. (2014). Privacy, anonymity, and big data in the social sciences. Communications of the ACM, 57(9), 56-63.

deLeeuw, E.D. (2008). Choosing the method of data collection. In E.D. deLeeuw, J.J. Hox, and D.A. Dillman (Eds.), International Handbook of Survey Methodology (pp. 113-135). New York: Lawrence Erlbaum Associates.

Deming, W.E. (1950). Some Theory of Sampling. New York: Dover.

Dinur, I., and Nissim, K. (2003). Revealing Information while Preserving Privacy. Available: http://www.cse.psu.edu/~ads22/privacy598/papers/dn03.pdf [November 2016].

Duncan, J.W. (1976). Confidentiality and the future of the U.S. statistical system. The American Statistician, 30(2), 54-59.

Duncan, J.W., and Shelton, W.C. (1992). U.S. government contributions to probability sampling and statistical analysis. Statistical Science, 7(3), 320-338.

Dwork, C. (2006). Differential privacy. In Automata, Languages and Programming (Vol. 4052) (pp. 1-12). Heidelberg, Germany: Springer. Available: http://research.microsoft.com/pubs/64346/dwork.pdf [November 2016].

Dwork, C., and Roth, A. (2014). The algorithmic foundations of differential privacy. Foundations and Trends in Theoretical Computer Science, 9(3-4), 211-407.

Dwork, C., McSherry, F., Nissim, K., and Smith, A. (2006). Calibrating noise to sensitivity in private data analysis. In S. Halevi and T. Rabin (Eds.), Theory of Cryptography (Vol. 3876) (pp. 265-284). Heidelberg, Germany: Springer. Available: http://link.springer.com/chapter/10.1007/11681878_14 [November 2016].

Dwork, C., McSherry, F., and Talwar, K. (2007). The price of privacy and the limits of LP decoding. In Proceedings of the 39th ACM Symposium on Theory of Computing (pp. 85-94). New York: ACM Publications. Available: http://dl.acm.org/citation.cfm?id=1250804 [November 2016].

Dwork, C., Naor, M., and Vadhan, S. (2012). The Privacy of the Analyst and the Power of the State. Proceedings of the 2012 IEEE 53rd Annual Symposium on Foundations of Computer Science (FOCS), New Brunswick, NJ, October 20-23. Available: http://privacytools.seas.harvard.edu/files/privacytools/files/06375318.pdf [November 2016].

Dwork, C., Smith, A., Steinke, T., Ullman, J., and Vadhan, S. (2015a). Robust Traceability from Trace Amounts. Proceedings of the 2015 IEEE 56th Annual Symposium Foundations of Computer Science (FOCS), Berkeley, CA, October 17-20. Available: http://privacytools.seas.harvard.edu/files/privacytools/files/robust.pdf?m=1445278897 [November 2016].

Dwork, C., Feldman, V., Hardt, M., Pitassi, O., Reingold, O., and Roth, A. (2015b). The reusable holdout: Preserving validity in adaptive data analysis. Science, 349(6248), 636-638.

Dwork, C., Smith, A., Steinke, T., and Ullam, J. (2017). Exposed! A survey of attacks on private data. Annual Review of Statistics and Its Application, 4.

The Economist. (2012). Don’t lie to me, Argentina. Available: http://www.economist.com/node/21548242 [November 2016].

Federal Committee on Statistical Methodology. (2006). Report on Statistical Disclosure Limitations Methodology. Statistical Policy Working Paper No. 22. Available: https://fcsm.sites.usa.gov/files/2014/04/spwp22.pdf [November 2016].

Fellegi, I.P. (1972). On the question of statistical confidentiality. Journal of the American Statistical Association, 67(337), 7-18.

Suggested Citation:"References." National Academies of Sciences, Engineering, and Medicine. 2017. Innovations in Federal Statistics: Combining Data Sources While Protecting Privacy. Washington, DC: The National Academies Press. doi: 10.17226/24652.
×

Freire, J., Bessa, A., Chirigati, F., Vo, H., and Zhao, K. (2016). Exploring What Not to Clean in Urban Data: A Study Using New York City Taxi Trips. New York: New York University. Available: http://sites.computer.org/debull/A16june/p63.pdf [November 2016].

Frias-Martinez, V., and Frias-Martinez, E. (2012). Enhancing Public Policy Decision Making Using Large-Scale Cell Phone Data. Available: www.unglobalpulse.org/publicpolicyandcellphonedata [November 2016].

Gellman, R. (2016). Fair Information Practices: A Basic History. Available: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2415020 [December 2016].

Gentry, C. (2009). A Fully Homomorphic Encryption Scheme. Ph.D. dissertation. Stanford, CA: Stanford University. Available: https://crypto.stanford.edu/craig/craig-thesis.pdf [November 2016].

Ginsberg, J., Mohebbi, M.H., Patel, R.S., Brammer, L., Smolinski, M.S., and Brilliant, L. (2009). Detecting influenza epidemics using search engine query data. Nature, 457(7232), 1012-1014.

Giorgi, E., Sesay, S.S., Terlouw, D.J., and Diggle, P.J. (2015). Combining data from multiple spatially referenced prevalence surveys using generalized linear geostatistical models. Journal of the Royal Statistical Society: Series A (Statistics in Society), 178(2), 445-464.

Goel, S., Hofman, J.M., Lahaie, S., Pennock, D.M., and Watts, D.J. (2010). Predicting consumer behavior with web search. Proceedings of the National Academy of Sciences, 107(41), 17486-17490.

Goerge, R.M., Smithgall, C., Seshadri, R., and Ballard, P. (2010). Illinois Families and Their Use of Multiple Service Systems. Chicago, IL: Chapin Hall. Available: https://www.chapinhall.org/sites/default/files/publications/Multiple%20Systems_IB_03_01_10_0.pdf [November 2016].

Goldreich, O., Micali, S., and Wigderson, A. (1987). How to play any mental game. In Proceedings of the 19th AMC Symposium on Theory of Computing (pp. 218-229). New York: ACM Publishing. Available: http://www.math.ias.edu/~avi/PUBLICATIONS/MYPAPERS/GMW87/GMW87.pdf [November 2016].

Grady, S., Bielick, S., and Aud, S. (2010). Trends in the Use of School Choice: 1993 to 2007. NCES 2010-004. Washington, DC: National Center for Education Statistics, Institute of Education Sciences, U.S. Department of Education. Available: http://nces.ed.gov/pubs2010/2010004.pdf [November 2016].

Groves, R. (2013). Improving government, academic and industry data-sharing opportunities. In J.A. Krosnick, S. Presser, K. Husbands Fealing, and S. Ruggles (Eds.), The Future of Survey Research: Challenges and Opportunities. Available: https://www.nsf.gov/sbe/AC_Materials/The_Future_of_Survey_Research.pdf [November 2016].

Habermann, H. (2010). Future of innovation in the federal statistical system. The ANNALS of the American Academy of Political and Social Science, 631(1), 194-203.

Haney, S., Machanavajjhala, A., Abowd, J., Graham, M., Kutzbach, M., and Vilhuber, L. (2017, Forthcoming). Utility Cost of Formal Privacy for Releasing National Employer-Employee Statistics. SIGMOD 2017, Raleigh, NC.

Hansen, M., Hurwitz, W.N., and Madow, W.G. (1953a). Sample Survey Methods and Theory, Volume 1. New York: John Wiley & Sons.

Hansen, M., Hurwitz, W.N., and Madow, W.G. (1953b). Sample Survey Methods and Theory, Volume 2. New York: John Wiley & Sons.

Hansen, M.H., Hurwitz, W.N., Nisselson, H., and Sternberg, J. (1955). The redesign of the Current Population Survey. Journal of the American Statistical Association, 50, 701-719.

Hartman, K., Habermann, H., Harris-Kojetin, B., Jones, C., Louis, T., and Gelman, A. (2014). Strength under pressure/A world without statistics. Significance, 11(4), 44-47.

Herzog, T.N., Scheuren, F.J., and Winkler, W.E. (2007). Data Quality and Record Linkage Techniques. New York: Springer.

Suggested Citation:"References." National Academies of Sciences, Engineering, and Medicine. 2017. Innovations in Federal Statistics: Combining Data Sources While Protecting Privacy. Washington, DC: The National Academies Press. doi: 10.17226/24652.
×

Hoff, N.G. (1981). Overview of the consumer expenditure surveys. Advances in Consumer Research, 8, 245-250.

Holdren, J.P. (2010). Social science data and the shaping of national policy. The ANNALS of the American Academy of Political and Social Science, 631(1), 18-21.

Holt, D.T. (2007). The official statistics Olympic challenge: Wider, deeper, quicker, better, cheaper. The American Statistician, 61(1), 1-8.

Homer, N., Szelinger, S., Redman, M., Duggan, D., Tembe, W., Muehling, J., Pearson, J., Stephan, D., Nelson, S., and Craig, D. (2008). Resolving individuals contributing trace amounts of DNA to highly complex mixtures using high-density SNP genotyping micro-arrays. PLoS Genetics, 4(8), e1000167. Available: http://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1000167 [November 2016].

Horrigan, M. (2013). Big Data and Official Statistics. Available: https://www.bls.gov/osmr/symp2013_horrigan.pdf [November 2016].

Hoyakem, C., Bollingerm, C., and Ziliak, J. (2014). The Role of CPS Nonresponse on the Level and Trend in Poverty. UKCPR Discussion Paper Series, DP 2014-05. Available: http://www.ukcpr.org/sites/www.ukcpr.org/files/documents/DP2014-05_0.pdf [November 2016].

Infas. (2010). Der Überwachte Bürger Zwischen Apathie Und Protest—Erste Ergebnisse. Available: http://www.vorratsdatenspeicherung.de/images/infas-umfrage.pdf [November 2016].

Institute of Medicine. (2009). Beyond the HIPAA Privacy Rule: Enhancing Privacy, Improving Health Through Research. S.J. Nass, L.A. Levit, and L.O. Gostin (Eds.). Board on Health Sciences Policy, Board on Health Care Services, Committee on Health Research and the Privacy of Health Information: The HIPAA Privacy Rule. Washington, DC: The National Academies Press.

Kasiviswanathan, S.P., Rudelson, M., and Smith, A. (2013). The Power of Linear Reconstruction Attacks. Proceedings of the 45th Annual ACM Symposium on the Theory of Computing, Palo Alto, CA, June 2-4. Available: https://arxiv.org/pdf/1210.2381v1.pdf [November 2016].

Kliss, B., and Scheuren, F.J. (1978). The 1973 CPS-IRS-SSA Exact Match Study. Social Security Bulletin, 51(7), 23-31.

Kraus, R. (2013). Statistical déjà vu: The National Data Center Proposal of 1965 and its descendants. Journal of Privacy and Confidentiality, 5(1), 1-37.

Lavallée, P. (2000). Combining Survey and Administrative Data: Discussion Paper. Proceedings of the Second International Conference on Establishment Surveys, Survey Methods for Businesses, Farms, and Institutions, Buffalo, NY.

Lazer, D., Kennedy, R., King, G., and Vespignani, A. (2014). The parable of Google Flu: Traps in big data analysis. Science, 343(6176), 1203-1205.

Lee, H., Warren, A., and Gill, L. (2015). Cheaper, Faster, Better: Are State Administrative Data the Answer? OPRE Report 2015-09. Available: http://www.acf.hhs.gov/sites/default/files/opre/mihope_strongstart_2yr_2015.pdf [November 2016].

Lohr, S.L., and Raghunathan, T.E. (in press). Combining survey data with other data sources. Statistical Science.

Louis, T.A. (2016). Discussion of Combining Information from Survey and non-Survey Data Sources: Challenges and Opportunities by Sharon Lohr and Trivellore Raghunathan. Proceedings of the 130th CNSTAT Meeting Public Seminar, Washington, DC, May 6. Available: http://sites.nationalacademies.org/cs/groups/dbassesite/documents/webpage/dbasse_172505.pdf [December 2016].

Lum, K., and Isaac, W. (2016). To predict and serve? Significance, 13, 14-19.

Suggested Citation:"References." National Academies of Sciences, Engineering, and Medicine. 2017. Innovations in Federal Statistics: Combining Data Sources While Protecting Privacy. Washington, DC: The National Academies Press. doi: 10.17226/24652.
×

Machanavajjhala, A., Kifer, D., Abowd, J., Gehrke, J., and Vilhuber, L. (2008). Privacy: Theory Meets Practice on the Map. Proceedings of the IEEE 24th International Conference on Data Engineering, Cancun, Mexico, April 7-12. Available: http://www.cse.psu.edu/~duk17/papers/PrivacyOnTheMap.pdf [November 2016].

Malone, S., and Mutikani, L. (2012). Jack Welch sets Twitter ablaze with Obama job jab. Chicago Tribune, October 5. Available: http://articles.chicagotribune.com/2012-10-05/news/sns-rt-us-usa-economy-jackwelchbre8941cr-20121005_1_tweet-jack-welch-alankrueger [November 2016].

Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., and Byers, A.H. (2011). Big data: The next frontier for innovation, competition, and productivity. McKinsey Global Institute, May. Available: http://www.mckinsey.com/business-functions/digital-mckinsey/our-insights/big-data-the-next-frontier-for-innovation [November 2016].

Manzi, G., Spiegelhalter, D.J., Turner, R.M., Flowers, J., and Thompson, S.G. (2011). Model-ling bias in combining small area prevalence estimates from multiple surveys. Journal of the Royal Statistical Society, 174(1), 31-50.

Marchetti, S., Giusti, C., Pratesi, M., Salvati, N., Giannotti, F., Pedreschi, D., Rinzivillo, S., Pappalardo, L., and Gabrielli, L. (2015). Small area model-based estimators using big data sources. Journal of Official Statistics, 31(2).

McPhee, C., Bielick, S., Masterton, M., Flores, L., Parmer, R., Amchin, S., Stern, S., and McGowan, H. (2015). National Household Education Surveys Program of 2012: Data File User’s Manual. NCES 2015-030. Washington, DC: National Center for Education Statistics, Institute of Education Sciences, U.S. Department of Education. Available: https://nces.ed.gov/nhes/pdf/userman/NHES_2012_UsersManual.pdf [November 2016].

Meyer, B.D., Mok, W.K.C., and Sullivan, J.X. (2015). Household surveys in crisis. Journal of Economic Perspectives, 29(4), 199-226.

Miller, P.V. (2010). Presidential address: The road to transparency in survey research. Public Opinion Quarterly, 74(3), 602-606.

Muthukrishnan, S., and Nikolov, A. (2012). Optimal private halfspace counting via discrepancy. In Proceedings of the Forty-Fourth Annual ACM Symposium on Theory of Computing (pp. 1285-1292). New York: ACM Publishing. Available: http://dl.acm.org/citation.cfm?id=2214090&dl=ACM&coll=DL&CFID=698830182&CFTOKEN=71109411 [November 2016].

Narayanan, A., and Shmatikov, V. (2008). Robust De-Anonymization of Large Sparse Datasets (How to Break Anonymity of the Netflix Prize Dataset). Proceedings of the 29th IEEE Symposium on Security and Privacy, Oakland, CA, May 18-21. Available: https://www.cs.cornell.edu/~shmat/shmat_oak08netflix.pdf [November 2016].

National Academies of Sciences, Engineering, and Medicine. (2016). Reducing Response Burden in the American Community Survey: Proceedings of a Workshop. Committee on National Statistics, Division of Behavioral and Social Sciences and Education. Washington, DC: The National Academies Press.

National Institutes of Health. (2016). National Institutes of Health Fiscal Year 2017 Budget Request. Available: https://officeofbudget.od.nih.gov/pdfs/FY17/31-Overview.pdf [November 2016].

National Research Council. (1979). Privacy and Confidentiality as Factors in Survey Response. Panel on Privacy and Confidentiality as Factors in Survey Response, Committee on National Statistics, Division of Behavioral and Social Sciences and Education. Washington, DC: National Academy Press.

Suggested Citation:"References." National Academies of Sciences, Engineering, and Medicine. 2017. Innovations in Federal Statistics: Combining Data Sources While Protecting Privacy. Washington, DC: The National Academies Press. doi: 10.17226/24652.
×

National Research Council. (1993). Private Lives and Public Policies: Confidentiality and Accessibility of Government Statistics. G.T. Duncan, T.B. Jabine, and V.A. De Wolf (Eds.). Panel on Confidentiality and Data Access, Committee on National Statistics, Division of Behavioral and Social Sciences and Education. Washington, DC: National Academy Press.

National Research Council. (2003). Statistical Issues in Allocating Funds by Formula. T.A. Louis, T.B. Jabine, and M.A. Gerstein (Eds.). Panel on Formula Allocations, Committee on National Statistics, Division of Behavioral and Social Sciences and Education. Washington, DC: The National Academies Press.

National Research Council. (2004). Reengineering the 2010 Census: Risks and Challenges. D.L. Cork, M.L. Cohen, and B.F. King (Eds.). Panel on Research on Future Census Methods, Committee on National Statistics, Division of Behavioral and Social Sciences and Education. Washington, DC: The National Academies Press.

National Research Council. (2005). Expanding Access to Research Data: Reconciling Risks and Opportunities. Panel on Data Access for Research Purposes, Committee on National Statistics, Division of Behavioral and Social Sciences and Education. Washington, DC: The National Academies Press.

National Research Council. (2007). Understanding Business Dynamics: An Integrated Data System for America’s Future. J. Haltiwanger, L.M. Lynch, and C. Mackie (Eds.). Panel on Measuring Business Formation, Dynamics, and Performance; Committee on National Statistics; Division of Behavioral and Social Sciences and Education. Washington, DC: The National Academies Press.

National Research Council. (2008). Using the American Community Survey for the National Science Foundation’s Science and Engineering Workforce Statistics Programs. Panel on Assessing the Benefits of the American Community Survey for the NSF Division of Science Resources Statistics, Committee on National Statistics, Division of Behavioral and Social Sciences and Education. Washington, DC: The National Academies Press.

National Research Council. (2009a). Ensuring the Quality, Credibility, and Relevance of U.S. Justice Statistics. R.M. Groves and D.L. Cork (Eds.). Panel to Review the Programs of the Bureau of Justice Statistics, Committee on National Statistics, Division of Behavioral and Social Sciences and Education. Washington, DC: The National Academies Press.

National Research Council. (2009b). Reengineering the Survey of Income and Program Participation. C.F. Citro and J.K. Scholz (Eds.). Panel on the Census Bureau’s Reengineered Survey of Income and Participation, Committee on National Statistics, Division of Behavioral and Social Sciences and Education. Washington, DC: The National Academies Press.

National Research Council. (2010a). Data on Federal Research and Development Investments: A Pathway to Modernization. Panel on Modernizing the Infrastructure of the National Science Foundation Federal Funds Survey, Committee on National Statistics, Division of Behavioral and Social Sciences and Education. Washington, DC: The National Academies Press.

National Research Council. (2010b). Envisioning the 2020 Census. L.D. Brown, M.L. Cohen, D.L. Cork, and C.F. Citro (Eds.). Panel on the Design of the 2020 Census Program of Evaluations and Experiments, Committee on National Statistics, Division of Behavioral and Social Sciences and Education. Washington, DC: The National Academies Press.

National Research Council. (2011). Change and the 2020 Census: Not Whether but How. Thomas M. Cook, Janet L. Norwood, and Daniel L. Cork (Eds.). Panel to Review the 2010 Census, Committee on National Statistics, Division of Behavioral and Social Sciences and Education. Washington, DC: The National Academies Press.

Suggested Citation:"References." National Academies of Sciences, Engineering, and Medicine. 2017. Innovations in Federal Statistics: Combining Data Sources While Protecting Privacy. Washington, DC: The National Academies Press. doi: 10.17226/24652.
×

National Research Council. (2013a). Nonresponse in Social Science Surveys: A Research Agenda. R. Tourangeau and T.J. Plewes (Eds.). Panel on a Research Agenda for the Future of Social Science Data Collection, Committee on National Statistics, Division of Behavioral and Social Sciences and Education. Washington, DC: The National Academies Press.

National Research Council. (2013b). Principles and Practices for a Federal Statistical Agency. Committee on National Statistics, Division of Behavioral and Social Sciences and Education. Washington, DC: The National Academies Press.

National Research Council. (2014a). Capturing Change in Science, Technology, and Innovation: Improving Indicators to Inform Policy. R.E. Litan, A.W. Wyckoff, and K.H. Fealing (Eds.). Panel on Developing Science, Technology, and Innovation Indicators for the Future; Board on Science, Technology, and Economic Policy; Division of Policy and Global Affairs. Washington, DC: The National Academies Press.

National Research Council. (2014b). Civic Engagement and Social Cohesion: Measuring Dimensions of Social Capital to Inform Policy. K. Prewitt, C.D. Mackie, and H. Habermann (Eds.). Panel on Measuring Social and Civic Engagement and Social Cohesion in Surveys, Committee on National Statistics, Division of Behavioral and Social Sciences and Education. Washington, DC: The National Academies Press.

National Research Council. (2015). Realizing the Potential of the American Community Survey: Challenges, Tradeoffs, and Opportunities. Panel on Addressing Priority Technical Issues for the Next Decade of the American Community Survey, Committee on National Statistics, Division of Behavioral and Social Sciences and Education. Washington, DC: The National Academies Press.

Norwood, J. (1995). Organizing to Count: Change in the Federal Statistical System. Washington, DC: Urban Institute Press.

Norwood, J. (2016). Politics and federal statistics. Statistics and Public Policy, 3(1), 1-8.

Parten, M. (1950). Surveys, Polls, and Samples: Practical Procedures. New York: Harper & Brothers.

Polivka, A.E., and Miller, S.M. (1995). The CPS After the Redesign: Refocusing the Economic Lens. Available: http://www.bls.gov/osmr/pdf/ec950090.pdf [November 2016].

Prell, M., Bradsher-Fredrick, H., Comisarow, C., Cornman, S., Cox, C., Denbaly, M., Martinez, R.W., Sabol, W., and Vile, M. (2009). Profiles in Success of Statistical Uses of Administrative Data. Available: http://www.bls.gov/osmr/fcsm.pdf [November 2016].

President’s Council of Advisors on Science and Technology. (2014). Big Data and Privacy: A Technological Perspective. Available: https://obamawhitehouse.archives.gov/sites/default/files/microsites/ostp/PCAST/pcast_big_data_and_privacy_-_may_2014.pdf [February 2017].

Prewitt, K. (2010). Science starts not after measurement, but with measurement. The ANNALS of the American Academy of Political and Social Science, 631(1), 7-16.

Puts, M.J.H., Tennekes, M., Daas, P.J.H., and Blois, C.D. (2016). Using Huge Amounts of Road Sensor Data for Official Statistics. Proceedings of the European Conference on Quality in Official Statistics (Q2016), Madrid, Spain. Available: http://www.pietdaas.nl/beta/pubs/pubs/q2016Final00177.pdf [November 2016].

Ramzy, A. (2016). Australia stops online collection of census data after cyberattacks. The New York Times, August 10. Available: http://www.nytimes.com/2016/08/11/world/australia/census-cyber-attack.html [November 2016].

Rand, M., and Catalano, S.M. (2007). Crime Victimization, 2006. Bureau of Justice Statistics, NCJ 219413. Available: http://www.bjs.gov/content/pub/pdf/cv06.pdf [November 2016].

Suggested Citation:"References." National Academies of Sciences, Engineering, and Medicine. 2017. Innovations in Federal Statistics: Combining Data Sources While Protecting Privacy. Washington, DC: The National Academies Press. doi: 10.17226/24652.
×

Reamer, A. (2014). Stumbling into the Great Recession: How and Why GDP Estimates Kept Economists and Policymakers in the Dark. Washington, DC: The George Washington Institute of Public Policy. Available: https://bea.gov/about/pdf/Reamer%20GDP%20Research%20Note%2004-25-14%20(1).pdf [November 2016].

Reamer, A., and Carpenter, R.B. (2010). Surveying for dollars: The role of the American Community Survey in the geographic distribution of federal funds. Brookings, July 26. Available: https://www.brookings.edu/research/surveying-for-dollars-the-role-of-the-american-community-survey-in-the-geographic-distribution-of-federal-funds [December 2016].

Roberts, D. (1997). Implementing the National Incident Based Reporting System: A Status Report. Bureau of Justice Statistics, NCJ 165581. Available: https://www.bjs.gov/content/pub/pdf/INIBRS.pdf [November 2016].

Robin, N., Klein, T., and Jütting, J. (2016). Public-Private Partnerships for Statistics: Lessons Learned, Future Steps: A Focus on the Use of Non-Official Data Sources for National Statistics and Public Policy. PARIS21, OECD Development Co-operation Working Papers, No. 27. Available: http://www.oecd-ilibrary.org/development/public-private-partnerships-for-statistics-lessons-learned-future-steps_5jm3nqp1g8wf-en [December 2016].

Rotenberg, M. (2000). Preserving Privacy in the Information Society. Available: http://www.unesco.org/webworld/infoethics_2/eng/papers/paper_10.htm [November 2016].

Sahai, A., and Waters, B. (2005). Fuzzy Identity-Based Encryption. Proceedings of Eurocrypt 2005, Aarhus, Denmark, May 22-26. Available: https://eprint.iacr.org/2004/086.pdf [December 2016].

Saslow, E. (2012). “Jobs Day”: Monthly release of employment data an economic, political obsession. Washington Post, March 9. Available: https://www.washingtonpost.com/national/jobs-day-an-economic-and-political-obsession/2012/03/09/gIQADZPW1R_story.html [November 2016].

Schenker, N., and Raghunathan, T.E. (2007). Combining information from multiple surveys to enhance estimation of measures of health. Statistics in Medicine, 26(8), 1802-1811.

Schulte Nordholt, E. (2014). Introduction to the Dutch Census 2011. In Dutch Census 2011: Analysis and Methodology (pp. 7-18). The Hague/Heerlen: Statistics Netherlands. Available: https://www.cbs.nl/NR/rdonlyres/5FDCE1B4-0654-45DA-8D7E807A0213DE66/0/2014b57pub.pdf [December 2016].

Seeskin, Z.H., and Spencer, B.D. (2015). Effect of Census Accuracy on Appointment of Congress and Allocations of Federal Funds. WP-15-05. Evanston, IL: Institute for Policy Research, Northwestern University. Available: http://www.ipr.northwestern.edu/publications/docs/workingpapers/2015/IPR-WP-15-05.pdf [November 2016].

Singer, E. (2003). The eleventh Morris Hansen lecture: Public perceptions of confidentiality. Journal of Official Statistics, 19(4), 333-341.

Singer, E., and Couper, M.P. (2010). Communicating disclosure risk in informed consent statements. Journal of Empirical Research on Human Research Ethics, 5(3), 1-8.

Singer, E., Hoewyk, J.V., and Neugebauer, R.J. (2003). Attitudes and behavior: The impact of privacy and confidentiality concerns on participation in the 2000 Census. Public Opinion Quarterly, 67(3), 368-384.

Solove, D.J., and Schwartz, P.M. (2015). Information Privacy Law (5th Edition). New York: Aspen Publishers.

Statistics Canada. (2016a). Creating a Modern Framework for an Independent National Statistics Office. Available: https://www.scribd.com/document/319364233/Statistics-Canada-recommendations-on-new-powers [November 2016].

Statistics Canada. (2016b). Model-based Principal Field Crop Estimates. Available: http://www23.statcan.gc.ca/imdb/p2SV.pl?Function=getSurvey&SDDS=5225#a1 [November 2016].

Suggested Citation:"References." National Academies of Sciences, Engineering, and Medicine. 2017. Innovations in Federal Statistics: Combining Data Sources While Protecting Privacy. Washington, DC: The National Academies Press. doi: 10.17226/24652.
×

Statistics Denmark. (2014). The Danish System for Access to Micro Data. Available: http://www.dst.dk/ext/645846915/0/forskning/Access-to-micro-data-at-Statistics-Denmark_2014.pdf [November 2016].

Struijs, P., and Daas, P. (2014). Quality Approaches to Big Data in Official Statistics. Proceedings for the European Conference on Quality in Official Statistics, Vienna, Austria, June 2-5. Available: http://www.pietdaas.nl/beta/pubs/pubs/Q2014_session_33_paper.pdf [November 2016].

Sweeney, L. (1997). Weaving technology and policy together to maintain confidentiality. The Journal of Law, Medicine & Ethics, 25(2-3), 98-110.

Sylvester, D., and Lohr, S. (2005). The security of our secrets: A history of privacy and confidentiality in law and statistical practice. Denver University Law Review, 83, 147-208. Available: http://www.law.du.edu/images/uploads/denver-university-law-review/v83_i1_sylvesterlohr.pdf [November 2016].

Tran, M. (2015). FBI Chief: “Unacceptable” That Guardian Has Better Data on Police Violence. Available: https://www.theguardian.com/us-news/2015/oct/08/fbi-chief-says-ridiculous-guardian-washington-post-better-information-police-shootings [November 2016].

Trépanier, J., Pignal, J., and Royce, D. (2013). Administrative Data Initiatives at Statistics Canada. Proceedings for the Federal Committee on Statistical Methodology Research Conference, Washington DC, November 4-6. Available: https://fcsm.sites.usa.gov/files/2014/05/G1_Trepanier_2013FCSM.pdf [November 2016].

Turn, R., and Ware, W.H. (1976). Privacy and Security Issues in Information Systems. Available: https://www.rand.org/content/dam/rand/pubs/papers/2008/P5684.pdf [November 2016].

U.N. Economic Commission for Europe. (2011). Using Administrative and Secondary Sources for Official Statistics: A Handbook of Principles and Practices. Available: http://www1.unece.org/stat/platform/display/adso/Using+Administrative+and+Secondary+Sources+for+Official+Statistics [November 2016].

U.N. Economic and Social Council. (2014). Report of the Global Group on Big Data for Official Statistics. Available: http://unstats.un.org/unsd/statcom/doc15/2015-4-BigData-E.pdf [November 2016].

U.N. Economic and Social Council. (2016). Report of the Global Working Group on Big Data for Official Statistics. Available: http://unstats.un.org/unsd/statcom/47th-session/documents/2016-6-Big-data-for-official-statistics-E.pdf [November 2016].

U.N. Global Pulse. (2014). Nowcasting Food Prices in Indonesia Using Social Media Signals. Global Pulse Project Series No. 1. Available: http://www.unglobalpulse.org/sites/default/files/UNGP_ProjectSeries_Nowcasting_Food_Prices_2014.pdf [November 2016].

U.N. Global Pulse. (2016). Postal Network’s Global Big Data Can Be Key to Understanding Nations’ Wellbeing. Available: http://unglobalpulse.org/news/postal-big-data-key-to-understanding-wellbeing [November 2016].

U.S. Census Bureau. (2015). U.S. Census Bureau’s Budget Estimates: Fiscal Year 2016. Available: http://www.osec.doc.gov/bmi/budget/FY16CJ/Census_2016_CJ.pdf [November 2016].

U.S. Department of Commerce. (2014). Fostering Innovation, Creating Jobs, Driving Better Decisions: The Value of Government Data. Economics and Statistics Administration. Available: http://esa.gov/sites/default/files/revisedfosteringinnovationcreatingjobsdrivingbetterdecisionsthevalueofgovernmentdata.pdf [November 2016].

U.S. Department of Health, Education, and Welfare. (1973). Records, Computers, and the Rights of Citizens. Available: https://www.justice.gov/opcl/docs/rec-com-rights.pdf [December 2016].

Suggested Citation:"References." National Academies of Sciences, Engineering, and Medicine. 2017. Innovations in Federal Statistics: Combining Data Sources While Protecting Privacy. Washington, DC: The National Academies Press. doi: 10.17226/24652.
×

U.S. Department of the Treasury. (2014). General Explanations of the Administration’s Fiscal Year 2015 Revenue Proposals. Available: https://www.treasury.gov/resourcecenter/tax-policy/Documents/General-Explanations-FY2015.pdf [November 2016].

U.S. Government Accountability Office. (2003). Formula Grants: 2000 Census Redistributes Federal Funding among States. GAO-03-178. Available: http://www.gao.gov/products/GAO-03-178 [November 2016].

U.S. Government Accountability Office. (2006). Federal Information Collection: A Reexamination of the Portfolio of Major Federal Household Surveys Is Needed. GAO-07-62. Available: http://www.gao.gov/products/GAO-07-62 [November 2016].

U.S. Government Accountability Office. (2009a). Formula Grants: Census Data Are among Several Factors That Can Affect Funding Allocations. GAO-09-832T. Available: http://www.gao.gov/products/GAO-09-832T [November 2016].

U.S. Government Accountability Office. (2009b). Funding for the Largest Federal Assistance Programs Is Based on Census-Related Data and Other Factors. GAO-10-263. Available: http://www.gao.gov/new.items/d10263.pdf [November 2016].

U.S. Office of Management and Budget. (1985). Statistical Policy Directive No. 3: Compilation, Release, and Evaluation of Principal Federal Economic Indicators. Available: https://obamawhitehouse.archives.gov/sites/default/files/omb/assets/omb/inforeg/statpolicy/dir_3_fr_09251985.pdf [February 2017].

U.S. Office of Management and Budget. (2006). Statistical Policy Directive No. 2: Standards and Guidelines for Statistical Surveys. Available: https://obamawhitehouse.archives.gov/sites/default/files/omb/inforeg/statpolicy/standards_stat_surveys.pdf [February 2017].

U.S. Office of Management and Budget. (2007). Implementation Guidance for the Title V of the E-Government Act, Confidential Information Protection and Statistical Efficiency Act of 2002 (CIPSEA). Available: https://obamawhitehouse.archives.gov/sites/default/files/omb/assets/omb/fedreg/2007/061507_cipsea_guidance.pdf [February 2017].

U.S. Office of Management and Budget. (2008). Statistical Policy Directive No. 4: Release and Dissemination of Statistical Products Produced by Federal Statistical Agencies. Available: https://obamawhitehouse.archives.gov/sites/default/files/omb/fedreg/2008/030708_directive-4.pdf [February 2017].

U.S. Office of Management and Budget. (2014a). M-14-06: Guidance for Providing and Using Administrative Data for Statistical Purposes. Available: https://obamawhitehouse.archives.gov/sites/default/files/omb/memoranda/2014/m-14-06.pdf [February 2017].

U.S. Office of Management and Budget. (2014b). Statistical Policy Directive No. 1: Fundamental Responsibilities of Federal Statistical Agencies and Recognized Statistical Units. Available: https://www.gpo.gov/fdsys/pkg/FR-2014-12-02/pdf/2014-28326.pdf [November 2016].

U.S. Office of Management and Budget. (2015a). Chapter 7: Building evidence with administrative data. In Analytical Perspectives: Budget of the United States Government: Fiscal Year 2016 (pp. 65-73). Washington, DC: Government Printing Office. Available: https://www.gpo.gov/fdsys/pkg/BUDGET-2016-PER/pdf/BUDGET-2016-PER-4-3.pdf [December 2016].

U.S. Office of Management and Budget. (2015b). Statistical Programs of the United States Government. Available: https://obamawhitehouse.archives.gov/sites/default/files/omb/assets/information_and_regulatory_affairs/statistical-programs-2016.pdf [February 2017].

U.S. Office of Management and Budget. (2016). Chapter 7: Building the capacity to produce and use evidence. In Analytical Perspectives: Budget of the United States Government: Fiscal Year 2017 (pp. 69-77). Washington, DC: Government Printing Office. Available: https://obamawhitehouse.archives.gov/sites/default/files/omb/budget/fy2017/assets/ap_7_evidence.pdf [February 2017].

Suggested Citation:"References." National Academies of Sciences, Engineering, and Medicine. 2017. Innovations in Federal Statistics: Combining Data Sources While Protecting Privacy. Washington, DC: The National Academies Press. doi: 10.17226/24652.
×

van Tuinen, H.K. (2009). Innovative statistics to improve our notion of reality. Journal of Official Statistics, 25(4), 431-465.

Wagner, D., and Layne, M. (2014). The Person Identification Validation System (PVS): Applying the Center for Administrative Records Research and Applications’ (CARRA) Record Linkage Software. Working Paper No. 2014-01. Available: https://www.census.gov/srd/carra/CARRA_PVS_Record_Linkage.pdf [November 2016].

Wallman, K.K., and Harris-Kojetin, B.A. (2004). Implementing the Confidential Information Protection and Statistical Efficiency Act of 2002. Chance, 17, 21-25.

Warren, J. (2016). Privacy Furore as Australians Prepare for Census. Available: http://www.forbes.com/sites/justinwarren/2016/08/04/privacy-furore-as-australians-prepare-for-census/#1378c7c7171c [November 2016].

Yang, S., Santillana, M., and Kou, S.C. (2015). Accurate estimation of influenza epidemics using Google search data via ARGO. Proceedings of the National Academy of Sciences, 112(47), 14473-14478. Available: www.pnas.org/cgi/doi/10.1073/pnas.1515373112 [December 2016].

Yates, F. (1949). Sampling Methods for Censuses and Surveys. London, UK: Charles Griffin and Company.

Zolas, N., Goldschlag, N., Jarmin, R., Stephan, P., Owen-Smith, J., Rosen, R.F., Allen, B.M., Weinberg, B.A., and Lane, J.I. (2015). Wrapping it up in a person: Examining employment and earnings outcomes for Ph.D. recipients. Science, 350(6266), 1367-1371.

Zukerberg, A. (2010). Redesigning the National Household Education Survey (NHES). Available: http://www.bls.gov/cex/aaporsrvyredesign2010zuckerb1.pdf [November 2016].

Suggested Citation:"References." National Academies of Sciences, Engineering, and Medicine. 2017. Innovations in Federal Statistics: Combining Data Sources While Protecting Privacy. Washington, DC: The National Academies Press. doi: 10.17226/24652.
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Federal government statistics provide critical information to the country and serve a key role in a democracy. For decades, sample surveys with instruments carefully designed for particular data needs have been one of the primary methods for collecting data for federal statistics. However, the costs of conducting such surveys have been increasing while response rates have been declining, and many surveys are not able to fulfill growing demands for more timely information and for more detailed information at state and local levels.

Innovations in Federal Statistics examines the opportunities and risks of using government administrative and private sector data sources to foster a paradigm shift in federal statistical programs that would combine diverse data sources in a secure manner to enhance federal statistics. This first publication of a two-part series discusses the challenges faced by the federal statistical system and the foundational elements needed for a new paradigm.

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