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All (25) (0 to 10 of 25 results)
- Articles and reports: 11F0019M2001176Geography: CanadaDescription:
Since the Job Vacancy Survey conducted by Statistics Canada between 1971 and 1978, there is no data which directly measures job vacancies in Canada. Using data from the 1999 Workplace and Employee Survey (WES), we attempt to fill this gap. We study the determinants of job vacancies at the location level. We find that workplaces with high vacancy rates consist of at least two types: 1) those employing a highly skilled workforce, innovating, adopting new technologies increasing skill requirements, facing significant international competition and operating in tight local labour markets, and 2) those which are non-unionized, operate in retail trade and consumer services industries and are not part of a multi-location firm. As a result, a substantial share of job vacancies are not in the high-technology sectors. More than 40% of all job vacancies and 50% of long-term vacancies originate from retail trade and consumer services industries.
Release date: 2001-11-01 - 2. The Effects of Inter-provincial Mobility on Individuals' Earnings: Panel Model Estimates for Canada ArchivedArticles and reports: 11F0019M2001163Geography: CanadaDescription:
This paper presents the findings of an empirical investigation of the effects of inter-provincial migration on individuals' earnings based on the newly available Longitudinal Administrative Database (LAD). The main results are based on a difference model which estimates the effects of mobility on (log) earnings which implicitly controls for initial earnings levels and other fixed effects, as well as other influences captured by the regressors included in the models. Inter-provincial mobility is found to be associated with statistically significant and in many cases quantitatively substantial changes in individuals' earnings, with these effects varying by age, sex, and province of origin. Pre- and post-move earnings profiles are also analysed, offering support for the validity of the difference model approach and indicating that movers are quickly integrated into local labour markets after their moves. Implications are discussed and possible directions for future research are suggested.
Release date: 2001-10-25 - Articles and reports: 11F0019M2001174Geography: CanadaDescription:
This paper investigates the evolution of the industrial structure in the Canadian manufacturing sector and its relationship to technological change by examining the take-up of advanced technologies and how it is related to the stochastic growth process in the plant population. Its framework is grounded in the view that growth is a stochastic process that involves learning. Experimentation with new technologies rewards some firms with superior growth and profitability. Examining how growth is associated with the choice of different technology strategies indicates which of these is being rewarded.
The evolution of this process is studied by examining the relationship between the uptake of advanced technologies and the performance of plants in the manufacturing sector. This is done by using cross-sectional data on advanced technology use and by combining it with longitudinal panel data on plant performance. In particular, the paper examines the relationship between the use of information and communications technology (ICT) and the growth in a plant's market share and its relative productivity.
The study finds that a considerable amount of market share is transferred from declining firms to growing firms over a decade. At the same time, the growers increase their productivity relative to the losers. Those technology users that were using communications technologies or that combined technologies from different classes increased their relative productivity the most. In turn, gains in relative productivity were accompanied by gains in market share. Other factors that were associated with gains in market share were the presence of R&D facilities and other innovative activities.
Release date: 2001-10-03 - 4. Regression composite estimation for the Canadian Labour Force Survey with a rotating panel design ArchivedArticles and reports: 12-001-X20010015852Description:
We consider the regression composite estimation introduced by Singh (1994, 1996; termed earlier as "modified regression composite" estimation), a version of which (suggested by Fuller 1999) has been implemented for the Canadian Labour Force Survey (CLFS) beginning in January 2000. The regression composite (rc) estimator enhances the generalized regression (gr) estimator used earlier for the CLFS and the well known Gurney-Daly ak-composite estimator in several ways.
Release date: 2001-08-22 - Articles and reports: 12-001-X20010015853Description:
The Canadian Labour Force Survey is a monthly survey of households selected according to a stratified multistage design. The sample of households is divided into six panels (rotation groups). A panel remains in the sample for six consecutive months and is then dropped from the sample. In the past, a generalized regression estimator, based only on the current month's data, has been implemented with a regression weights program. In this paper, we study regression composite estimation procedures that make use of sample information from previous periods and that can be implemented with a regression weights program.
Release date: 2001-08-22 - Articles and reports: 12-001-X20010015854Description:
This paper looks at a range of estimators applicable to a regularly repeated household survey with controlled overlap between successive surveys. The paper shows how the Best Linear Unbiased Estimator (BLUE) based on a fixed window of time points can be improved by applying the technique of generalised regression. This improved estimator is compared to the AK estimator of Gurney and Daly (1965) and the modified regression estimator of Singh, Kennedy, Wu and Brisebois (1997), using data from the Australian Labour Force Survey.
Release date: 2001-08-22 - 7. Regression composite estimation for the Canadian Labour Force Survey: Evaluation and implementation ArchivedArticles and reports: 12-001-X20010015855Description:
The Canadian Labour Force Survey (LFS) is a monthly survey with a complex rotating panel design. After extensive studies, including the investigation of a number of alternative methods for exploiting the sample overlap to improve the quality of estimates, the LFS has chosen a composite estimation method which achieves this goal while satisfying practical constraints. In addition, for variables where there is a substantial gain in efficiency, the new time series tend to make more sense from a subject-matter perspective. This makes it easier to explain LFS estimates to users and the media. Because of the reduced variance under composite estimation, for some variables it is now possible to publish monthly estimates where only three-month moving averages were published in the past. In addition, a greater number of series can be successfully seasonally adjusted.
Release date: 2001-08-22 - 8. Variance estimation after imputation ArchivedArticles and reports: 12-001-X20010015856Description:
Imputation is commonly used to compensate for item nonresponse. Variance estimation after imputation has generated considerable discussion and several variance estimators have been proposed. We propose a variance estimator based on a pseudo data set used only for variance estimation. Standard complete data variance estimators applied to the pseudo data set lead to consistent estimators for linear estimators under various imputation methods, including without-replacement hot deck imputation and with-replacement hot deck imputation. The asymptotic equivalence of the proposed method and the adjusted jackknife method of Rao and Sitter (1995) is illustrated. The proposed method is directly applicable to variance estimation for two-phase sampling.
Release date: 2001-08-22 - 9. A multivariate technique for multiply imputing missing values using a sequence of regression models ArchivedArticles and reports: 12-001-X20010015857Description:
This article describes and evaluates a procedure for imputing missing values for a relatively complex data structure when the data are missing at random. The imputations are obtained by fitting a sequence of regression models and drawing values from the corresponding predictive distributions. The types of regression models used are linear, logistic, Poisson, generalized logit or a mixture of these depending on the type of variable being imputed. Two additional common features in the imputation process are incorporated: restriction to a relevant subpopulation for some variables and logical bounds or constraints for the imputed values. The restrictions involve subsetting the sample individuals that satisfy certain criteria while fitting the regression models. The bounds involve drawing values from a truncated predictive distribution. The development of this method was partly motivated by the analysis of two data sets which are used as illustrations. The sequential regression procedure is applied to perform multiple imputation analysis for the two applied problems. The sampling properties of inferences from multiply imputed data sets created using the sequential regression method are evaluated through simulated data sets.
Release date: 2001-08-22 - Articles and reports: 12-001-X20010015859Description:
In 2001, the INSEE conducted a survey to better understand the homeless population. Since there was no survey frame to allow direct access to homeless persons, the survey principle involved sampling the services they received and questioning the individuals who used those services. Weighting the individual input to the survey proved difficult because a single individual could receive several services within the designated reference period. This article shows how it is possible to apply the weight sharing method to resolve this problem. In this type of survey, a single variable can produce several parameters of interest corresponding to populations varying with time. A set of weights corresponds to each definition of parameters. The article focuses, in particular, on "an average day" and "an average week" weight calculation. Information is also provided on the use data to be collected and the nonresponse adjustment.
Release date: 2001-08-22
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Analysis (24)
Analysis (24) (0 to 10 of 24 results)
- Articles and reports: 11F0019M2001176Geography: CanadaDescription:
Since the Job Vacancy Survey conducted by Statistics Canada between 1971 and 1978, there is no data which directly measures job vacancies in Canada. Using data from the 1999 Workplace and Employee Survey (WES), we attempt to fill this gap. We study the determinants of job vacancies at the location level. We find that workplaces with high vacancy rates consist of at least two types: 1) those employing a highly skilled workforce, innovating, adopting new technologies increasing skill requirements, facing significant international competition and operating in tight local labour markets, and 2) those which are non-unionized, operate in retail trade and consumer services industries and are not part of a multi-location firm. As a result, a substantial share of job vacancies are not in the high-technology sectors. More than 40% of all job vacancies and 50% of long-term vacancies originate from retail trade and consumer services industries.
Release date: 2001-11-01 - 2. The Effects of Inter-provincial Mobility on Individuals' Earnings: Panel Model Estimates for Canada ArchivedArticles and reports: 11F0019M2001163Geography: CanadaDescription:
This paper presents the findings of an empirical investigation of the effects of inter-provincial migration on individuals' earnings based on the newly available Longitudinal Administrative Database (LAD). The main results are based on a difference model which estimates the effects of mobility on (log) earnings which implicitly controls for initial earnings levels and other fixed effects, as well as other influences captured by the regressors included in the models. Inter-provincial mobility is found to be associated with statistically significant and in many cases quantitatively substantial changes in individuals' earnings, with these effects varying by age, sex, and province of origin. Pre- and post-move earnings profiles are also analysed, offering support for the validity of the difference model approach and indicating that movers are quickly integrated into local labour markets after their moves. Implications are discussed and possible directions for future research are suggested.
Release date: 2001-10-25 - Articles and reports: 11F0019M2001174Geography: CanadaDescription:
This paper investigates the evolution of the industrial structure in the Canadian manufacturing sector and its relationship to technological change by examining the take-up of advanced technologies and how it is related to the stochastic growth process in the plant population. Its framework is grounded in the view that growth is a stochastic process that involves learning. Experimentation with new technologies rewards some firms with superior growth and profitability. Examining how growth is associated with the choice of different technology strategies indicates which of these is being rewarded.
The evolution of this process is studied by examining the relationship between the uptake of advanced technologies and the performance of plants in the manufacturing sector. This is done by using cross-sectional data on advanced technology use and by combining it with longitudinal panel data on plant performance. In particular, the paper examines the relationship between the use of information and communications technology (ICT) and the growth in a plant's market share and its relative productivity.
The study finds that a considerable amount of market share is transferred from declining firms to growing firms over a decade. At the same time, the growers increase their productivity relative to the losers. Those technology users that were using communications technologies or that combined technologies from different classes increased their relative productivity the most. In turn, gains in relative productivity were accompanied by gains in market share. Other factors that were associated with gains in market share were the presence of R&D facilities and other innovative activities.
Release date: 2001-10-03 - 4. Regression composite estimation for the Canadian Labour Force Survey with a rotating panel design ArchivedArticles and reports: 12-001-X20010015852Description:
We consider the regression composite estimation introduced by Singh (1994, 1996; termed earlier as "modified regression composite" estimation), a version of which (suggested by Fuller 1999) has been implemented for the Canadian Labour Force Survey (CLFS) beginning in January 2000. The regression composite (rc) estimator enhances the generalized regression (gr) estimator used earlier for the CLFS and the well known Gurney-Daly ak-composite estimator in several ways.
Release date: 2001-08-22 - Articles and reports: 12-001-X20010015853Description:
The Canadian Labour Force Survey is a monthly survey of households selected according to a stratified multistage design. The sample of households is divided into six panels (rotation groups). A panel remains in the sample for six consecutive months and is then dropped from the sample. In the past, a generalized regression estimator, based only on the current month's data, has been implemented with a regression weights program. In this paper, we study regression composite estimation procedures that make use of sample information from previous periods and that can be implemented with a regression weights program.
Release date: 2001-08-22 - Articles and reports: 12-001-X20010015854Description:
This paper looks at a range of estimators applicable to a regularly repeated household survey with controlled overlap between successive surveys. The paper shows how the Best Linear Unbiased Estimator (BLUE) based on a fixed window of time points can be improved by applying the technique of generalised regression. This improved estimator is compared to the AK estimator of Gurney and Daly (1965) and the modified regression estimator of Singh, Kennedy, Wu and Brisebois (1997), using data from the Australian Labour Force Survey.
Release date: 2001-08-22 - 7. Regression composite estimation for the Canadian Labour Force Survey: Evaluation and implementation ArchivedArticles and reports: 12-001-X20010015855Description:
The Canadian Labour Force Survey (LFS) is a monthly survey with a complex rotating panel design. After extensive studies, including the investigation of a number of alternative methods for exploiting the sample overlap to improve the quality of estimates, the LFS has chosen a composite estimation method which achieves this goal while satisfying practical constraints. In addition, for variables where there is a substantial gain in efficiency, the new time series tend to make more sense from a subject-matter perspective. This makes it easier to explain LFS estimates to users and the media. Because of the reduced variance under composite estimation, for some variables it is now possible to publish monthly estimates where only three-month moving averages were published in the past. In addition, a greater number of series can be successfully seasonally adjusted.
Release date: 2001-08-22 - 8. Variance estimation after imputation ArchivedArticles and reports: 12-001-X20010015856Description:
Imputation is commonly used to compensate for item nonresponse. Variance estimation after imputation has generated considerable discussion and several variance estimators have been proposed. We propose a variance estimator based on a pseudo data set used only for variance estimation. Standard complete data variance estimators applied to the pseudo data set lead to consistent estimators for linear estimators under various imputation methods, including without-replacement hot deck imputation and with-replacement hot deck imputation. The asymptotic equivalence of the proposed method and the adjusted jackknife method of Rao and Sitter (1995) is illustrated. The proposed method is directly applicable to variance estimation for two-phase sampling.
Release date: 2001-08-22 - 9. A multivariate technique for multiply imputing missing values using a sequence of regression models ArchivedArticles and reports: 12-001-X20010015857Description:
This article describes and evaluates a procedure for imputing missing values for a relatively complex data structure when the data are missing at random. The imputations are obtained by fitting a sequence of regression models and drawing values from the corresponding predictive distributions. The types of regression models used are linear, logistic, Poisson, generalized logit or a mixture of these depending on the type of variable being imputed. Two additional common features in the imputation process are incorporated: restriction to a relevant subpopulation for some variables and logical bounds or constraints for the imputed values. The restrictions involve subsetting the sample individuals that satisfy certain criteria while fitting the regression models. The bounds involve drawing values from a truncated predictive distribution. The development of this method was partly motivated by the analysis of two data sets which are used as illustrations. The sequential regression procedure is applied to perform multiple imputation analysis for the two applied problems. The sampling properties of inferences from multiply imputed data sets created using the sequential regression method are evaluated through simulated data sets.
Release date: 2001-08-22 - Articles and reports: 12-001-X20010015859Description:
In 2001, the INSEE conducted a survey to better understand the homeless population. Since there was no survey frame to allow direct access to homeless persons, the survey principle involved sampling the services they received and questioning the individuals who used those services. Weighting the individual input to the survey proved difficult because a single individual could receive several services within the designated reference period. This article shows how it is possible to apply the weight sharing method to resolve this problem. In this type of survey, a single variable can produce several parameters of interest corresponding to populations varying with time. A set of weights corresponds to each definition of parameters. The article focuses, in particular, on "an average day" and "an average week" weight calculation. Information is also provided on the use data to be collected and the nonresponse adjustment.
Release date: 2001-08-22
Reference (1)
Reference (1) ((1 result))
- 1. Chain Fisher formula ArchivedNotices and consultations: 13-605-X20010018529Description:
As of May 31, 2001 the Quarterly Income and Expenditure Accounts will have adopted the following change: Chain Fisher formula.
Release date: 2001-05-31
- Date modified: