.

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EB

EB

ONLINE

Cham Springer International Publishing, 2017

1 online zdroj

ISBN 978-3-319-53745-0 (e-kniha)

ISBN 978-3-319-53744-3 (print)

This textbook focuses on the cohort change ratio (CCR) method. It presents powerful, yet relatively simple ways to generate accurate demographic estimates and forecasts that are cost efficient and require fewer resources than other techniques. The concepts, analytical frameworks, and methodological tools presented do not require extensive knowledge of demographics, mathematics, or statistics. The demographic focus is on the characteristics of populations, especially age and sex composition, but these methods are applicable estimating and forecasting other characteristics and total population. The book contains more traditional applications such as the Hamilton-Perry method, but also includes new applications of the CCR method such as stable population theory. Real world empirical examples are provided for every application; along with excel files containing data and program code, which are accessible online. Topics covered include basic demographic measures, sources of demographic information, forecasting and estimating (both current and historical) populations, modifications to current methods, forecasting school enrollment and other characteristics, estimating life expectancy, stable population theory, decomposition of the CCR into its migration and mortality components, and the utility of the CCR. This textbook is designed to provide material for an advanced undergraduate or graduate course on demographic methods. It can also be used as a supplement for other courses including applied demography, business and economic forecasting and market research..

* kohorta * cohort * cohort change ratio

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1 Introduction 1 // 1.1 Why a Book on Cohort Change Ratios? 1 // 1.2 Cohorts and Their Analyses 2 // 1.3 The Cohort Change Ratio 2 // 1.4 Reverse CCRs 6 // 1.5 Census Survival Ratios 7 // 1.6 Some Applications of Cohort Change Ratios 7 // 1.7 About This Book 8 // References 10 // 2 Basic Demographic Concepts 13 // 2.1 Introduction 13 // 2.2 Estimates, Projections, and Forecasts 13 // 2.3 Demographic Concepts 14 // 2.3.1 Size 14 // 2.3.2 Distribution 15 // 2.3.3 Composition 17 // 2.3.4 Change 19 // 2.4 Statistical Measures 24 // 2.4.1 Ratios 24 // 2.4.2 Rates and Probabilities 26 // 2.4.3 The Odds Ratio 29 // 2.5 Participation-Rate Method 30 // 2.5.1 Logic and Formulas 30 // 2.5.2 Implementation Issues 31 // References 31 // 3 Sources of Demographic Information 35 // 3.1 Introduction 35 // 3.2 United States Census Bureau 36 // 6.5 Evaluation 94 // 6.5.1 Age Groups 95 // 6.5.2 Total Population 97 // 6.6 Conclusions 98 // Appendix 100 // Cohort Change Ratios and the Fundamental Demographie // Equation 100 // References 102 // 7 Forecasting School Enrollment Size and Composition 107 // 7.1 Introduction 107 // 7.2 Short-Term Enrollment Forecasting by Grade 108 // 7.3 Long-Term Student Population and Enrollment Forecasting // by Grade 110 // 7.4 Evaluation 115 // 7.5 Conclusions 117 // References 117 // 8 Forecasting Other Characteristics 119 // 8.1 Introduction 119 // 8.2 Studies Using the Participation-Rate Method 120 // 8.2.1 Disability in the United States 120 // 8.2.2 Obesity in the United States 120 // 8.2.3 Cardiovascular Disease in the United States 121 // 8.3 Developing Population-Related Forecasts 123 // 8.3.1 Alcohol Consumption in the United States 123 // 8.3.2 Diabetes in the United States 125 // 8.3.3 Cigarette Use and Consumption in the United States 128 // 8.3.4 Civilian Labor Force Forecast for San Diego County, California 131 //

119 // 8.1 Introduction 119 // 8.2 Studies Using the Participation-Rate Method 120 // 8.2.1 Disability in the United States 120 // 8.2.2 Obesity in the United States 120 // 8.2.3 Cardiovascular Disease in the United States 121 // 8.3 Developing Population-Related Forecasts 123 // 8.3.1 Alcohol Consumption in the United States 123 // 8.3.2 Diabetes in the United States 125 // 8.3.3 Cigarette Use and Consumption in the United States 128 // 8.3.4 Civilian Labor Force Forecast for San Diego County, California 131 // 8.3.5 Other Population and Housing Variables for San Diego County, California 134 // 8.4 Conclusions 137 // References 139 // 9 Estimating Population Size and Composition 143 // 9.1 Introduction 143 // 9.2 Interpolation Methods 144 // 9.3 Examples 145 // 9.4 Conclusions 150 // References 150 // 10 Estimating Historical Populations 151 // 10.1 Introduction 151 // 10.2 Reverse Cohort Change Ratios 151 // 3.3 Decennial Census 36 // 3.4 Population Estimates 39 // 3.5 Surveys 39 // 3.6 Administrative Records 41 // 3.7 International Data 42 // 3.8 Other Data Sources 42 // 3.9 On-line Location of Excel Files 43 // 3.10 Conclusions 44 // References 44 // 4 Forecasting Population Size and Composition 45 // 4.1 Introduction 45 // 4.2 Hamilton-Perry Forecast 46 // 4.2.1 Forecast by Age and Gender 46 // 4.2.2 Forecast by Age 48 // 4.2.3 Forecast of Major League Pitchers 49 // 4.3 Controlling a Hamilton-Perry Forecast 52 // 4.4 Conclusions 56 // References 57 // 5 Forecasting Using Modified Cohort Change Ratios 59 // 5.1 Introduction 59 // 5.2 Modifying Cohort Change and Child-Woman Ratios 60 // 5.3 Measures of Forecast Error 62 // 5.4 Empirical Data 63 // 5.5 Empirical Results 65 // 5.5.1 Total Population Forecast Error 65 // 5.5.2 Forecast Error by Age Group 67 // 5.5.3 Total Population Forecast Error by Population Size and Growth Rate 74 // 5.6 Conclusions 80 // References 82 //

6 Forecasting Uncertainty 83 // 6.1 Introduction 83 // 6.2 Forecast Uncertainty 83 // 6.3 Statistical Forecast Intervals 84 // 6.3.1 Model-Based Intervals 84 // 6.3.2 Empirically-Based Intervals 86 // 6.4 Statistical Intervals for Cohort Change Ratios and Population Forecasts 89 // 6.4.1 Statistical Inference and the Concept of a // Super-Population 89 // 6.4.2 Hamilton-Perry Method 89 // 6.4.3 Incorporating Uncertainty into the Hamilton-Perry Method 91 // 10.3 Examples 153 // 10.3.1 1910 Native Hawaiian Population Estimates in Hawai’i 153 // 10.3.2 1770 to 1900 Native Hawaiian Population Estimates in Hawai’i 153 // 10.3.3 CCRs and Life Table Survival Rates 159 // 10.3.4 Multi-racial Population Estimates for San Bernardino // and Riverside Counties 160 // 10.4 Conclusions 162 // References 163 // 11 Estimating Life Expectancy 165 // 11.1 Introduction 165 // 11.2 Estimating Life Expectancy 165 // 11.3 Life Expectancy: The United Nations Census Survival Method 166 // 11.4 Estimating Life Expectancy from Cohort Change Ratios 167 // 11.5 Empirical Examples and Evaluation 168 // 11.6 Conclusions 170 // Appendix 170 // Relation Between Survival Rates and Life Expectancy 170 // References 171 // 12 Stable Population Theory 173 // 12.1 Introduction 173 // 12.2 Cohort Change Ratios and the Stable Population Model 174 // 12.3 Illustration of Stable Populations with and without Migration 175 // 12.4 Impact of Demographic Components of Change on Convergence 179 // 12.5 Other Strategies to Analyze Convergence 182 // 12.5.1 Clarifying Measures of Convergence: Transient or Asymptotic Dynamics 182 // 12.5.2 Components of Change: Interactions and Convergence 183 // 12.5.3 Perturbation Analysis and the Life Table Response Experiment Framework 186 // 12.6 Conclusions 187 // References 187 // 13 Decompositions 191 // 13.1 Introduction 191 // 13.2 Decompositions 192 //

13.2.1 Subgroup Decomposition 192 // 13.2.2 Components of Change Decomposition 193 // 13.2.3 Subgroup and the Components of Change // Decomposition 194 // 13.3 Applications 195 // 13.3.1 Contribution of Subgroup CCRs to the Total CCR 195 // 13.3.2 Indirect Forecasts of the Components of Change 197 // 13.3.3 Contribution of Subgroup Components of Change to Total Population Change 203 // 13.4 Conclusions 206 // References 207 // 14 Forecasting with Spatial Dependencies 209 // 14.1 Introduction 209 // 14.2 Issues with Georeferenced Data 210 // 14.3 Modeling Spatial Dependencies: Spatial Weights Matrices 210 // 14.3.1 Defining a Geographic Neighborhood 211 // 14.3.2 Constructing and Using a Spatial Weights Matrix 213 // 14.4 Spatially-Weighted Hamilton-Perry Forecast 215 // 14.5 Alternative Spatial Approaches 216 // 14.6 Boundary Changes 219 // 14.7 Conclusions 220 // References 220 // 15 The Utility of Cohort Change Ratios 225 // 15.1 Introduction 225 // 15.2 The Concept of Utility 225 // 15.3 Utility and the Cohort Change Ratio Method 227 // 15.4 Conclusions 236 // References 245 // 16 Concluding Remarks 247 // 16.1 Introduction 247 // 16.2 Top Ten Reasons to Use the CCR Method 248 // References 250 // Appendix 251 // Cohort Change Ratios and the Fundamental Demographic Equation 251 // Index 253