Abraham, K.G., J.C. Haltiwanger, K. Sandusky, and J.R. Spletzer. 2018. “Measuring the Gig Economy: Current Knowledge and Open Issues.” National Bureau of Economic Research Working Paper No. 24950. August. Doi: http://dx.doi.org/10.3386/w24950 .
Abraham, K.G. and S.N. Houseman. 2018. “Making Ends Meet: The Role of Informal Work in Supplementing Americans’ Income.” Upjohn Institute, unpublished working paper. December. Available at: https://www.aeaweb.org/conference/2019/preliminary/paper/QreAaS2h (accessed July 2019).
://unstats.un.org/unsd/envaccounting/ceea/archive/EPEA/EnvIndustry_Manual_for_data_collection.PDF (accessed May 2016).
ONS. 2015. “UK Environmental Goods and Services Sector (EGSS): 2010–2012.” Available at: http://www.ons.gov.uk/economy/environmentalaccounts/bulletins/ukenvironmentalaccounts/2015-04-15 (accessed October 2014).
Ramage, D., D. Hall, R. Nallapati, and C.D. Manning. 2009. “Labeled LDA: A Supervised Topic Model for Credit Attribution in Multi-labeled Corpora.” Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, 248–256, Singapore, August 6–7, 2009. Available at: https://www.aclweb.org/anthology/D09
Elena Dalla Chiara, Martina Menon and Federico Perali
’Italia . Dipartimento di Politiche Pubbliche e Scelte Collettive – POLIS, Università del Piemonte Orientale (Working Paper n. 63). Available at: http://polis.unipmn.it/pubbl/RePEc/uca/ucapdv/sisto63.pdf (accessed April 2015).
Slesnick, D. 1993. “Gaining Ground: Poverty in the Postwar United States.” Journal of Political Economy 101: 1–38. Doi: http://dx.doi.org/10.1086/261864 .
Smith, J. and P. Todd. 2005. “Rejoinder.” Journal of Econometrics 125: 365–375. Doi: http://dx.doi.org/10.1016/j.jeconom.2004.04.013 .
Stiglitz, E., A. Sen, and J.P. Fitoussi. 2010
. “Multilevel Analysis of the Effects of Antidiscrimination Policies on Earnings by Sexual Orientation.” Journal of Policy Analysis and Management 30(2): 334–358. DOI: https://doi.org/10.1002/pam.20563 .
Klawitter, M. 2014. “Meta-Analysis of the Effects of Sexual Orientation on Earnings.” Industrial Relations: A Journal of Economy and Society 54(1): 4–32. DOI: https://doi.org/10.1111/irel.12075 .
Kreider, R.M. and D.A. Lofquist. 2015. “Matching Survey Data with Administrative Records to Evaluate Reports of Same-Sex Married Couple Households.” SEHSD Working
FitzGerald, J. 2014. “Additional Data Needed to Understand Developments in the Economy.” Quarterly Economic Commentary 2014: 34-25.
Friedman, T. 2006. The World is Flat: The Globalized World in the Twenty-First Century. London: Penguin Books.
Girardin, F., F. Dal Fiore, C. Ratti, and J. Blat. 2008. “Leveraging Explicitly Disclosed Location Information to Understand Tourism Dynamics: A Case Study.” Journal of Location Based Services 2: 41-56.
Greenwald, G. 2015. No Place to Hide: Edward Snowden, the NSA and the Surveillance
This article, delivered as the 22nd Memorial Morris Hansen lecture, argues that the contract houses, typified by Westat, are uniquely situated in the cluster of institutions, practices, and principles that collectively constitute a bridge between scientific evidence on the one hand and public policy on the other. This cluster is defined in The Use of Science as Evidence in Public Policy as a policy enterprise that generates a form of social knowledge on which modern economies, policies, and societies depend (National Research Council 2012).
The policy enterprise in the U. S. largely took shape in the first half of the twentieth century, when sample surveys and inferential statistics matured into an information system that provided reliable and timely social knowledge relevant to the nation’s policy choices. In ways described shortly, Westat and other social science organizations that respond to “request for proposals” (RFP) from the government for social data and social analysis came to occupy a unique niche.
The larger question addressed is whether the policy enterprise as we know it is prepared for the tsunami beginning to encroach on its territory. Is it going to be swamped by a data tsunami that takes information from very different sources than the familiar census/survey methods?
In this article we propose a methodology for estimating the GDP of a country’s different regions, providing quarterly profiles for the annual official observed data. Thus the article offers a new instrument for short-term monitoring that allows the analysts to quantify the degree of synchronicity among regional business cycles. Technically, we combine time-series models with benchmarking methods to process short-term quarterly indicators and to estimate quarterly regional GDPs ensuring their temporal and transversal consistency with the National Accounts data. The methodology addresses the issue of nonadditivity, explicitly taking into account the transversal constraints imposed by the chain-linked volume indexes used by the National Accounts, and provides an efficient combination of structural as well as short-term information. The methodology is illustrated by an application to the Spanish economy, providing real-time quarterly GDP estimates, that is, with a minimum compilation delay with respect to the national quarterly GDP. The estimated quarterly data are used to assess the existence of cycles shared among the Spanish regions.
Measures of period-to-period change are key statistics for many economy surveys. To improve the precision of these estimates of change, the majority of the business surveys at Statistics Sweden select stratified simple random samples (STSI) at different points in time, ensuring positive correlation between samples (overlapping samples) by using permanent random numbers (PRN). Statistics Sweden normally selects positively coordinated STSIs drawn from an updated Business Register (BR). In these samples, the industry strata are usually stratified further within industry into size strata. When the most recent sampling frame contains updated classification variables for all units, enterprises can change stratum between two sampling occasions. A drawback of the coordinated sample selection procedure is that the desired correlation between the two samples decreases if the proportion of enterprises that change strata is substantial. Consequently, the sample design must anticipate the potential effect of stratum changes between samples. This article presents a study that examines how the design of a repeated business survey affects the precision in estimates of change over time using the Turnover in the Service Sector survey conducted by Statistics Sweden as an example.
Albacete, N., P. Lindner, K. Wagner, and S. Zottel. 2012. “Eurosystem Finance and Consumption Survey 2010: Methodological Notes for Austria.” Addendum to Monetary Policy and the Economy Q3/12: 1-100.
Andreasch, M., P. Fessler, and M. Schürz. 2009. “Austrian Households’ Equity Capital - Evidence from Microdata.” Monetary Policy and the Economy Q4/09: 61-78.
Antoniewicz, R., R. Bonci, A. Generale, G. Marchese, A. Neri, K. Maser, and P. O’Hagan. 2005. “Household Wealth: Comparing Micro and Macro Data in
.” NBER Working Paper 11633. Doi: http://dx.doi.org/10.3386/w11633 .
Blendon, R.J., J.M. Benson, M. Brodie, R. Morin, D.E. Altman, D. Gitterman, M. Brossard, and M. James. 1997. “Bridging the Gap between the Public’s and Economists’ Views of the Economy.” Journal of Economic Perspectives 11: 105–118. Doi: http://dx.doi.org/10.1257/jep.11.3.105 .
Blinder, A.S. and A.B. Krueger. 2004. “What does the Public Know about Economic Policy, and How Does It Know It?” Brookings Papers on Economic Activity 1: 327–387. Doi: http://dx.doi.org/10.3386/w10787