The Experts below are selected from a list of 63528 Experts worldwide ranked by ideXlab platform

Rikiya Matsukura - One of the best experts on this subject based on the ideXlab platform.

  • Multidimensional Life Table Estimation of the Total Fertility Rate and Its Components
    Demography, 2012
    Co-Authors: Robert D. Retherford, Naohiro Ogawa, Hassan Eini-zinab, Minja Kim Choe, Rikiya Matsukura
    Abstract:

    Using discrete-time survival models of parity progression and illustrative data from the Philippines, this article develops a multivariate multidimensional life table of nuptiality and Fertility, the dimensions of which are age, parity, and duration in parity. The measures calculated from this life table include total Fertility Rate (TRF), total marital Fertility Rate (TMFR), parity progression ratios (PPR), age-specific Fertility Rates, mean and median ages at first marriage, mean and median closed birth intervals, and mean and median ages at childbearing by child’s birth order and for all birth orders combined. These measures are referred to collectively as “TFR and its components.” Because the multidimensional life table is multivariate, all measures derived from it are also multivariate in the sense that they can be tabulated by categories or selected values of one socioeconomic variable while controlling for other socioeconomic variables. The methodology is applied to birth history data, in the form of actual birth histories from a Fertility survey or reconstructed birth histories derived from a census or household survey. The methodology yields period estimates as well as cohort estimates of the aforementioned measures.

  • Multivariate analysis of parity progression-based measures of the total Fertility Rate and its components.
    Demography, 2010
    Co-Authors: Robert D. Retherford, Naohiro Ogawa, Rikiya Matsukura, Hassan Eini-zinab
    Abstract:

    This article describes a methodology for applying a discrete-time survival model—the complementary log-log model—to estimate effects of socioeconomic variables on (1) the total Fertility Rate and its components and (2) trends in the total Fertility Rate and its components. For the methodology to be applicable, the total Fertility Rate (TFR) must be calculated from parity progression ratios (PPRs). The components of the TFR are PPRs, the total marital Fertility Rate (TMFR), and the TFR itself as measures of the quantum of Fertility, and mean and median ages at first marriage and mean and median closed birth intervals by birth order as measures of the tempo or timing of Fertility. The focus is on effects of predictor variables on these measures rather than on coefficients, which are often difficult to interpret in the complex models that are considered. The methodology is applicable to both period and cohort data. It is illustRated by application to data from the 1993, 1998, and 2003 Demographic and Health Surveys (DHS) in the Philippines.

  • multivariate analysis of parity progression based measures of the total Fertility Rate and its components using individual level data
    2006
    Co-Authors: Robert D. Retherford, Naohiro Ogawa, Rikiya Matsukura
    Abstract:

    This paper develops multivariate methods for analyzing (1) effects of socioeconomic variables on the total Fertility Rate and its components and (2) effects of socioeconomic variables on the trend in the total Fertility Rate and its components. For the multivariate methods to be applicable the total Fertility Rate must be calculated from parity progression ratios (PPRs) pertaining to transitions from birth to first marriage first marriage to first birth first birth to second birth and so on. The components of the TFR that are considered include PPRs the total marital Fertility Rate (TMFR) and the TFR itself as measures of the quantum of Fertility and mean and median ages at first marriage and mean and median closed birth intervals by birth order as measures of the tempo or timing of Fertility. The multivariate methods are applicable to both period measures and cohort measures of these quantities. The methods are illustRated by application to data from the 1993 1998 and 2003 Demographic and Health Surveys (DHS) in the Philippines. (authors)

Hassan Eini-zinab - One of the best experts on this subject based on the ideXlab platform.

  • Multidimensional Life Table Estimation of the Total Fertility Rate and Its Components
    Demography, 2012
    Co-Authors: Robert D. Retherford, Naohiro Ogawa, Hassan Eini-zinab, Minja Kim Choe, Rikiya Matsukura
    Abstract:

    Using discrete-time survival models of parity progression and illustrative data from the Philippines, this article develops a multivariate multidimensional life table of nuptiality and Fertility, the dimensions of which are age, parity, and duration in parity. The measures calculated from this life table include total Fertility Rate (TRF), total marital Fertility Rate (TMFR), parity progression ratios (PPR), age-specific Fertility Rates, mean and median ages at first marriage, mean and median closed birth intervals, and mean and median ages at childbearing by child’s birth order and for all birth orders combined. These measures are referred to collectively as “TFR and its components.” Because the multidimensional life table is multivariate, all measures derived from it are also multivariate in the sense that they can be tabulated by categories or selected values of one socioeconomic variable while controlling for other socioeconomic variables. The methodology is applied to birth history data, in the form of actual birth histories from a Fertility survey or reconstructed birth histories derived from a census or household survey. The methodology yields period estimates as well as cohort estimates of the aforementioned measures.

  • Multivariate analysis of parity progression-based measures of the total Fertility Rate and its components.
    Demography, 2010
    Co-Authors: Robert D. Retherford, Naohiro Ogawa, Rikiya Matsukura, Hassan Eini-zinab
    Abstract:

    This article describes a methodology for applying a discrete-time survival model—the complementary log-log model—to estimate effects of socioeconomic variables on (1) the total Fertility Rate and its components and (2) trends in the total Fertility Rate and its components. For the methodology to be applicable, the total Fertility Rate (TFR) must be calculated from parity progression ratios (PPRs). The components of the TFR are PPRs, the total marital Fertility Rate (TMFR), and the TFR itself as measures of the quantum of Fertility, and mean and median ages at first marriage and mean and median closed birth intervals by birth order as measures of the tempo or timing of Fertility. The focus is on effects of predictor variables on these measures rather than on coefficients, which are often difficult to interpret in the complex models that are considered. The methodology is applicable to both period and cohort data. It is illustRated by application to data from the 1993, 1998, and 2003 Demographic and Health Surveys (DHS) in the Philippines.

Robert D. Retherford - One of the best experts on this subject based on the ideXlab platform.

  • Multidimensional Life Table Estimation of the Total Fertility Rate and Its Components
    Demography, 2012
    Co-Authors: Robert D. Retherford, Naohiro Ogawa, Hassan Eini-zinab, Minja Kim Choe, Rikiya Matsukura
    Abstract:

    Using discrete-time survival models of parity progression and illustrative data from the Philippines, this article develops a multivariate multidimensional life table of nuptiality and Fertility, the dimensions of which are age, parity, and duration in parity. The measures calculated from this life table include total Fertility Rate (TRF), total marital Fertility Rate (TMFR), parity progression ratios (PPR), age-specific Fertility Rates, mean and median ages at first marriage, mean and median closed birth intervals, and mean and median ages at childbearing by child’s birth order and for all birth orders combined. These measures are referred to collectively as “TFR and its components.” Because the multidimensional life table is multivariate, all measures derived from it are also multivariate in the sense that they can be tabulated by categories or selected values of one socioeconomic variable while controlling for other socioeconomic variables. The methodology is applied to birth history data, in the form of actual birth histories from a Fertility survey or reconstructed birth histories derived from a census or household survey. The methodology yields period estimates as well as cohort estimates of the aforementioned measures.

  • Multivariate analysis of parity progression-based measures of the total Fertility Rate and its components.
    Demography, 2010
    Co-Authors: Robert D. Retherford, Naohiro Ogawa, Rikiya Matsukura, Hassan Eini-zinab
    Abstract:

    This article describes a methodology for applying a discrete-time survival model—the complementary log-log model—to estimate effects of socioeconomic variables on (1) the total Fertility Rate and its components and (2) trends in the total Fertility Rate and its components. For the methodology to be applicable, the total Fertility Rate (TFR) must be calculated from parity progression ratios (PPRs). The components of the TFR are PPRs, the total marital Fertility Rate (TMFR), and the TFR itself as measures of the quantum of Fertility, and mean and median ages at first marriage and mean and median closed birth intervals by birth order as measures of the tempo or timing of Fertility. The focus is on effects of predictor variables on these measures rather than on coefficients, which are often difficult to interpret in the complex models that are considered. The methodology is applicable to both period and cohort data. It is illustRated by application to data from the 1993, 1998, and 2003 Demographic and Health Surveys (DHS) in the Philippines.

  • multivariate analysis of parity progression based measures of the total Fertility Rate and its components using individual level data
    2006
    Co-Authors: Robert D. Retherford, Naohiro Ogawa, Rikiya Matsukura
    Abstract:

    This paper develops multivariate methods for analyzing (1) effects of socioeconomic variables on the total Fertility Rate and its components and (2) effects of socioeconomic variables on the trend in the total Fertility Rate and its components. For the multivariate methods to be applicable the total Fertility Rate must be calculated from parity progression ratios (PPRs) pertaining to transitions from birth to first marriage first marriage to first birth first birth to second birth and so on. The components of the TFR that are considered include PPRs the total marital Fertility Rate (TMFR) and the TFR itself as measures of the quantum of Fertility and mean and median ages at first marriage and mean and median closed birth intervals by birth order as measures of the tempo or timing of Fertility. The multivariate methods are applicable to both period measures and cohort measures of these quantities. The methods are illustRated by application to data from the 1993 1998 and 2003 Demographic and Health Surveys (DHS) in the Philippines. (authors)

S N Dang - One of the best experts on this subject based on the ideXlab platform.

  • Fertility Rate and the prediction of future population size in Shaanxi province
    Chinese journal of epidemiology, 2013
    Co-Authors: Wei Wang, Fan Xj, Liu R, S N Dang
    Abstract:

    OBJECTIVE: To analyze the Fertility Rate and to estimate the future population size of Shaanxi province based on data from the sixth national population census. METHODS: Fertility Rate curve was used to analyze the Fertility model and the abbreviated life table. The actual Fertility Rate was used as the main way to predict the future population size. General Fertility Rate was analyzed by factor analysis approach. RESULTS: The total Fertility Rate of Shaanxi province was 1.05 in 2010 while age-specific Fertility Rate contributed 101.27% to the general Fertility Rate. The expected population sizes would be 38 122 474 in 2015 38 432 931 in 2020 and 38 121 904 in 2025 respectively. CONCLUSION: BirthRate would become lower and the population size appearing a negative increase in the year 2020 in Shaanxi province.

Naohiro Ogawa - One of the best experts on this subject based on the ideXlab platform.

  • Multidimensional Life Table Estimation of the Total Fertility Rate and Its Components
    Demography, 2012
    Co-Authors: Robert D. Retherford, Naohiro Ogawa, Hassan Eini-zinab, Minja Kim Choe, Rikiya Matsukura
    Abstract:

    Using discrete-time survival models of parity progression and illustrative data from the Philippines, this article develops a multivariate multidimensional life table of nuptiality and Fertility, the dimensions of which are age, parity, and duration in parity. The measures calculated from this life table include total Fertility Rate (TRF), total marital Fertility Rate (TMFR), parity progression ratios (PPR), age-specific Fertility Rates, mean and median ages at first marriage, mean and median closed birth intervals, and mean and median ages at childbearing by child’s birth order and for all birth orders combined. These measures are referred to collectively as “TFR and its components.” Because the multidimensional life table is multivariate, all measures derived from it are also multivariate in the sense that they can be tabulated by categories or selected values of one socioeconomic variable while controlling for other socioeconomic variables. The methodology is applied to birth history data, in the form of actual birth histories from a Fertility survey or reconstructed birth histories derived from a census or household survey. The methodology yields period estimates as well as cohort estimates of the aforementioned measures.

  • Multivariate analysis of parity progression-based measures of the total Fertility Rate and its components.
    Demography, 2010
    Co-Authors: Robert D. Retherford, Naohiro Ogawa, Rikiya Matsukura, Hassan Eini-zinab
    Abstract:

    This article describes a methodology for applying a discrete-time survival model—the complementary log-log model—to estimate effects of socioeconomic variables on (1) the total Fertility Rate and its components and (2) trends in the total Fertility Rate and its components. For the methodology to be applicable, the total Fertility Rate (TFR) must be calculated from parity progression ratios (PPRs). The components of the TFR are PPRs, the total marital Fertility Rate (TMFR), and the TFR itself as measures of the quantum of Fertility, and mean and median ages at first marriage and mean and median closed birth intervals by birth order as measures of the tempo or timing of Fertility. The focus is on effects of predictor variables on these measures rather than on coefficients, which are often difficult to interpret in the complex models that are considered. The methodology is applicable to both period and cohort data. It is illustRated by application to data from the 1993, 1998, and 2003 Demographic and Health Surveys (DHS) in the Philippines.

  • multivariate analysis of parity progression based measures of the total Fertility Rate and its components using individual level data
    2006
    Co-Authors: Robert D. Retherford, Naohiro Ogawa, Rikiya Matsukura
    Abstract:

    This paper develops multivariate methods for analyzing (1) effects of socioeconomic variables on the total Fertility Rate and its components and (2) effects of socioeconomic variables on the trend in the total Fertility Rate and its components. For the multivariate methods to be applicable the total Fertility Rate must be calculated from parity progression ratios (PPRs) pertaining to transitions from birth to first marriage first marriage to first birth first birth to second birth and so on. The components of the TFR that are considered include PPRs the total marital Fertility Rate (TMFR) and the TFR itself as measures of the quantum of Fertility and mean and median ages at first marriage and mean and median closed birth intervals by birth order as measures of the tempo or timing of Fertility. The multivariate methods are applicable to both period measures and cohort measures of these quantities. The methods are illustRated by application to data from the 1993 1998 and 2003 Demographic and Health Surveys (DHS) in the Philippines. (authors)