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The Dutch 2011 Census tables were produced by combining existing register and sample survey data. Since the last census based on a complete enumeration was held in 1971, the willingness of the population to participate has fallen sharply. Statistics Netherlands no longer uses census questionnaires and has found an alternative in the register-based census, using only existing data. The register-based census is cheaper and more socially acceptable. The table results of the Netherlands are not only comparable with earlier Dutch censuses, but also with those of the other countries in the 2011 European Census Round, although most other countries do not yet have register-based censuses. The Dutch Census 2011 cannot be considered in isolation from its predecessors. This most recent census builds on a rich tradition of traditional population and housing censuses carried out in the period 1829–1971. For the European Union censuses of 1981 and 1991, Statistics Netherlands provided alternative census data consisting of a combination of register and survey data, but without complete numerical consistency between census tables and without detailed information about regions and specific categories. This combined data provision was continued for the censuses of 2001 and 2011, by which time it was possible to compile completely consistent data at a very detailed level. The 2011 Census data are more detailed than those of 2001, and include the hypercubes (high dimensional tables) that are mandatory for all EU countries under the 2008 European Census Act. The challenge for the future is to keep comparability, but to improve the timeliness of the European census results. [ABSTRACT FROM AUTHOR]

Previous studies have shown that modeling based on administrative records can be predictive of Nonresponse Followup (NRFU) enumeration outcomes in U.S. Census Bureau Decennial data collection operations. We compare model predictive power when varying training data sources and evaluate the extent to which survey data can be used to reduce enumerator workload when combined with available administrative data. We perform the evaluation using the 2010 Census and the 2014 American Community Survey. Our main finding is that a large survey-based training dataset, such as the American Community Survey, can provide results comparable to Census data. Robustness checks then illustrate that even small sample survey-based training datasets can also yield comparable predictions. We also discuss a broader role for use of existing survey data in NRFU operations of statistical agencies outside of the United States when national Census or administrative data sources have only incomplete coverage of the population. [ABSTRACT FROM AUTHOR]

The aim of this paper is to show a Palestinian Central Bureau of Statistics (PCBS) [1] experiment in administrative data records linkage. We focused in this paper on PCBS experiment in matching different data sources from different ministries, municipalities and other partners with PCBS Establishments Census 2012. Different matching algorithms and tools were used in the experiment. We started our experiment by using the Fuzzy Lookup [2]. It is an add-in for Excel developed by Microsoft Research. It performs fuzzy matching of textual data in Microsoft Excel. The tool uses the Jaccard Index of Similarity and Levenshtein distance; a statistical way to measure similarities between sample sets. In order to compare data and try to find out matching data, we also used Duke, see Lars [3] which is an existing and flexible deduplication (or entity resolution, or record linkage) engine written in Java. By using Duke Engine, we wrote our matching algorithm and comparators to increase the matching results and matching accuracy. We also wrote some data-cleaning functions for matching variables (Commercial Name, Owner Name and Telephone) in order to standardize each matching variable to get improved results. Different matching algorithms were used in the experiment such as Hamming Distance, e.g. Mohammad [4], Levenshtein distance, Mark [5], Jaccard Similarity, e.g. Suphakit et al. [6], exact match and multiple match. The results showed that after cleaning the identification variables, the number of matches rises significantly. We also noted that there is an improvement in matching rates when going from the matching based only on phone numbers to the matching based on Telephone, Commercial Name and Owner Name. [ABSTRACT FROM AUTHOR]

Previous studies have shown that modeling based on administrative records can be predictive of Nonresponse Followup (NRFU) enumeration outcomes in U.S. Census Bureau Decennial data collection operations. We compare model predictive power when varying training data sources and evaluate the extent to which survey data can be used to reduce enumerator workload when combined with available administrative data. We perform the evaluation using the 2010 Census and the 2014 American Community Survey. Our main finding is that a large survey-based training dataset, such as the American Community Survey, can provide results comparable to Census data. Robustness checks then illustrate that even small sample survey-based training datasets can also yield comparable predictions. We also discuss a broader role for use of existing survey data in NRFU operations of statistical agencies outside of the United States when national Census or administrative data sources have only incomplete coverage of the population. [ABSTRACT FROM AUTHOR]