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- Articles and reports: 11-633-X2021007Description:
Statistics Canada continues to use a variety of data sources to provide neighbourhood-level variables across an expanding set of domains, such as sociodemographic characteristics, income, services and amenities, crime, and the environment. Yet, despite these advances, information on the social aspects of neighbourhoods is still unavailable. In this paper, answers to the Canadian Community Health Survey on respondents’ sense of belonging to their local community were pooled over the four survey years from 2016 to 2019. Individual responses were aggregated up to the census tract (CT) level.
Release date: 2021-11-16 - Articles and reports: 11-522-X202100100018Description: Statistics Finland started publishing nowcasts of the trend indicator of output (TIO), the monthly indicator of real economic activity, to answer users´ needs during the Covid-19 pandemic. The indicator was first published in April 2020, at the very beginning of the pandemic in Finland, and had a monthly release schedule until June 2021. The TIO nowcasts are produced using open-source data on truck traffic volumes at about 100 automatic measuring points in the Helsinki/Uusimaa -region and the Economic Sentiment Indicator for Finland. Estimation is done using a machine learning approach and the methodology is based on previous work done by Statistics Finland and ETLA Economic Research.
Key Words: nowcasting; flash estimates; machine learning; experimental statistics.
Release date: 2021-10-29 - Articles and reports: 11-522-X202100100025Description:
We propose a longitudinal analysis with a point of view connected to the organizational changes that have taken place in the Italian National Institute of Statistics in recent years. In 2016 the Institute introduced a new Directorate, intending to standardize and generalize the business process of Data Collection according to the European standard of the GAMSO model. The paper discusses the pros and cons of this change from the perspective of the survey's participation. The ICT survey response rate analysis demonstrates an increase of around 20% since the beginning of the new organization: the paper tries to focus on the impact of the changes introduced with the new organization. We focused our attention on two specific subsets of respondents - the so-called "wanted" - the ones who have never answered to an ICT survey or to any other Istat survey and - the so-called “lost” - the ones included in two consecutive survey’s samples and that answered in the previous edition but not in the current one. The paper aims to illustrate how an efficient organization of data collection reflects its benefits on survey results and what kind of actions should be taken to catch the attention of the "wanted". Finally, we apply a logistic model measuring the probability that an enterprise responding in 2018 (t-1) also answered in 2019 (t). All the analysis suggests some actions that could be taken to improve respondents' participation, data quality, and respondents' perception of the official statistics.
Key Words: data collection strategy, response rate, paradata, response burden, ICT Survey.
Release date: 2021-10-29 - Articles and reports: 11-522-X202100100005Description: The Permanent Census of Population and Housing is the new census strategy adopted in Italy in 2018: it is based on statistical registers combined with data collected through surveys specifically designed to improve registers quality and assure Census outputs. The register at the core of the Permanent Census is the Population Base Register (PBR), whose main administrative sources are the Local Population Registers. The population counts are determined correcting the PBR data with coefficients based on the coverage errors estimated with surveys data, but the need for additional administrative sources clearly emerged while processing the data collected with the first round of Permanent Census. The suspension of surveys due to global-pandemic emergency, together with a serious reduction in census budget for next years, makes more urgent a change in estimation process so to use administrative data as the main source. A thematic register has been set up to exploit all the additional administrative sources: knowledge discovery from this database is essential to extract relevant patterns and to build new dimensions called signs of life, useful for population estimation. The availability of the collected data of the two first waves of Census offers a unique and valuable set for statistical learning: association between surveys results and ‘signs of life’ could be used to build classification model to predict coverage errors in PBR. This paper present the results of the process to produce ‘signs of life’ that proved to be significant in population estimation.
Key Words: Administrative data; Population Census; Statistical Registers; Knowledge discovery from databases.
Release date: 2021-10-22 - Articles and reports: 11-522-X202100100014Description: Recent developments in questionnaire administration modes and data extraction have favored the use of nonprobability samples, which are often affected by selection bias that arises from the lack of a sample design or self-selection of the participants. This bias can be addressed by several adjustments, whose applicability depends on the type of auxiliary information available. Calibration weighting can be used when only population totals of auxiliary variables are available. If a reference survey that followed a probability sampling design is available, several methods can be applied, such as Propensity Score Adjustment, Statistical Matching or Mass Imputation, and doubly robust estimators. In the case where a complete census of the target population is available for some auxiliary covariates, estimators based in superpopulation models (often used in probability sampling) can be adapted to the nonprobability sampling case. We studied the combination of some of these methods in order to produce less biased and more efficient estimates, as well as the use of modern prediction techniques (such as Machine Learning classification and regression algorithms) in the modelling steps of the adjustments described. We also studied the use of variable selection techniques prior to the modelling step in Propensity Score Adjustment. Results show that adjustments based on the combination of several methods might improve the efficiency of the estimates, and the use of Machine Learning and variable selection techniques can contribute to reduce the bias and the variance of the estimators to a greater extent in several situations.
Key Words: nonprobability sampling; calibration; Propensity Score Adjustment; Matching.
Release date: 2021-10-15 - Articles and reports: 11-522-X202100100019Description: Official statistical agencies must continually seek new methods and techniques that can increase both program efficiency and product relevance. The U.S. Census Bureau’s measurement of construction activity is currently a resource-intensive endeavor, relying heavily on monthly survey response via questionnaires and extensive field data collection. While our data users continually require more timely and granular data products, the traditional survey approach and associated collection cost and respondent burden limits our ability to meet that need. In 2019, we began research on whether the application of machine learning techniques to satellite imagery could accurately estimate housing starts and completions while meeting existing monthly indicator timelines at a cost equal to or less than existing methods. Using historical Census construction survey data in combination with targeted satellite imagery, the team trained, tested, and validated convolutional neural networks capable of classifying images by their stage of construction demonstrating the viability of a data science-based approach to producing official measures of construction activity.
Key Words: Official Statistics; Housing Starts, Machine Learning, Satellite Imagery
Release date: 2021-10-15
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Articles and reports (6)
Articles and reports (6) ((6 results))
- Articles and reports: 11-633-X2021007Description:
Statistics Canada continues to use a variety of data sources to provide neighbourhood-level variables across an expanding set of domains, such as sociodemographic characteristics, income, services and amenities, crime, and the environment. Yet, despite these advances, information on the social aspects of neighbourhoods is still unavailable. In this paper, answers to the Canadian Community Health Survey on respondents’ sense of belonging to their local community were pooled over the four survey years from 2016 to 2019. Individual responses were aggregated up to the census tract (CT) level.
Release date: 2021-11-16 - Articles and reports: 11-522-X202100100018Description: Statistics Finland started publishing nowcasts of the trend indicator of output (TIO), the monthly indicator of real economic activity, to answer users´ needs during the Covid-19 pandemic. The indicator was first published in April 2020, at the very beginning of the pandemic in Finland, and had a monthly release schedule until June 2021. The TIO nowcasts are produced using open-source data on truck traffic volumes at about 100 automatic measuring points in the Helsinki/Uusimaa -region and the Economic Sentiment Indicator for Finland. Estimation is done using a machine learning approach and the methodology is based on previous work done by Statistics Finland and ETLA Economic Research.
Key Words: nowcasting; flash estimates; machine learning; experimental statistics.
Release date: 2021-10-29 - Articles and reports: 11-522-X202100100025Description:
We propose a longitudinal analysis with a point of view connected to the organizational changes that have taken place in the Italian National Institute of Statistics in recent years. In 2016 the Institute introduced a new Directorate, intending to standardize and generalize the business process of Data Collection according to the European standard of the GAMSO model. The paper discusses the pros and cons of this change from the perspective of the survey's participation. The ICT survey response rate analysis demonstrates an increase of around 20% since the beginning of the new organization: the paper tries to focus on the impact of the changes introduced with the new organization. We focused our attention on two specific subsets of respondents - the so-called "wanted" - the ones who have never answered to an ICT survey or to any other Istat survey and - the so-called “lost” - the ones included in two consecutive survey’s samples and that answered in the previous edition but not in the current one. The paper aims to illustrate how an efficient organization of data collection reflects its benefits on survey results and what kind of actions should be taken to catch the attention of the "wanted". Finally, we apply a logistic model measuring the probability that an enterprise responding in 2018 (t-1) also answered in 2019 (t). All the analysis suggests some actions that could be taken to improve respondents' participation, data quality, and respondents' perception of the official statistics.
Key Words: data collection strategy, response rate, paradata, response burden, ICT Survey.
Release date: 2021-10-29 - Articles and reports: 11-522-X202100100005Description: The Permanent Census of Population and Housing is the new census strategy adopted in Italy in 2018: it is based on statistical registers combined with data collected through surveys specifically designed to improve registers quality and assure Census outputs. The register at the core of the Permanent Census is the Population Base Register (PBR), whose main administrative sources are the Local Population Registers. The population counts are determined correcting the PBR data with coefficients based on the coverage errors estimated with surveys data, but the need for additional administrative sources clearly emerged while processing the data collected with the first round of Permanent Census. The suspension of surveys due to global-pandemic emergency, together with a serious reduction in census budget for next years, makes more urgent a change in estimation process so to use administrative data as the main source. A thematic register has been set up to exploit all the additional administrative sources: knowledge discovery from this database is essential to extract relevant patterns and to build new dimensions called signs of life, useful for population estimation. The availability of the collected data of the two first waves of Census offers a unique and valuable set for statistical learning: association between surveys results and ‘signs of life’ could be used to build classification model to predict coverage errors in PBR. This paper present the results of the process to produce ‘signs of life’ that proved to be significant in population estimation.
Key Words: Administrative data; Population Census; Statistical Registers; Knowledge discovery from databases.
Release date: 2021-10-22 - Articles and reports: 11-522-X202100100014Description: Recent developments in questionnaire administration modes and data extraction have favored the use of nonprobability samples, which are often affected by selection bias that arises from the lack of a sample design or self-selection of the participants. This bias can be addressed by several adjustments, whose applicability depends on the type of auxiliary information available. Calibration weighting can be used when only population totals of auxiliary variables are available. If a reference survey that followed a probability sampling design is available, several methods can be applied, such as Propensity Score Adjustment, Statistical Matching or Mass Imputation, and doubly robust estimators. In the case where a complete census of the target population is available for some auxiliary covariates, estimators based in superpopulation models (often used in probability sampling) can be adapted to the nonprobability sampling case. We studied the combination of some of these methods in order to produce less biased and more efficient estimates, as well as the use of modern prediction techniques (such as Machine Learning classification and regression algorithms) in the modelling steps of the adjustments described. We also studied the use of variable selection techniques prior to the modelling step in Propensity Score Adjustment. Results show that adjustments based on the combination of several methods might improve the efficiency of the estimates, and the use of Machine Learning and variable selection techniques can contribute to reduce the bias and the variance of the estimators to a greater extent in several situations.
Key Words: nonprobability sampling; calibration; Propensity Score Adjustment; Matching.
Release date: 2021-10-15 - Articles and reports: 11-522-X202100100019Description: Official statistical agencies must continually seek new methods and techniques that can increase both program efficiency and product relevance. The U.S. Census Bureau’s measurement of construction activity is currently a resource-intensive endeavor, relying heavily on monthly survey response via questionnaires and extensive field data collection. While our data users continually require more timely and granular data products, the traditional survey approach and associated collection cost and respondent burden limits our ability to meet that need. In 2019, we began research on whether the application of machine learning techniques to satellite imagery could accurately estimate housing starts and completions while meeting existing monthly indicator timelines at a cost equal to or less than existing methods. Using historical Census construction survey data in combination with targeted satellite imagery, the team trained, tested, and validated convolutional neural networks capable of classifying images by their stage of construction demonstrating the viability of a data science-based approach to producing official measures of construction activity.
Key Words: Official Statistics; Housing Starts, Machine Learning, Satellite Imagery
Release date: 2021-10-15
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