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E-raamat: Cohort Change Ratios and their Applications

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  • Formaat: PDF+DRM
  • Ilmumisaeg: 21-Apr-2017
  • Kirjastus: Springer International Publishing AG
  • Keel: eng
  • ISBN-13: 9783319537450
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Cohort Change Ratios and their ApplicationsSuurem pilt
  • Formaat: PDF+DRM
  • Ilmumisaeg: 21-Apr-2017
  • Kirjastus: Springer International Publishing AG
  • Keel: eng
  • ISBN-13: 9783319537450
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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 demograph

ic 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.

Foreword.- Preface.- Chapter 1 Introduction.- Chapter 2 Basic Demographic Concepts.- Chapter 3 Sources of Demographic Information.- Chapter 4 Forecasting Population Size and Composition.- Chapter 5 Forecasting Using Modified Cohort Change Ratios.- Chapter 6 Forecasting Uncertainty.- Chapter 7 Forecasting School Enrollment Size and Composition.- Chapter 8 Forecasting Other Characteristics.- Chapter 9 Estimating Population Size and Composition.- Chapter 10 Estimating Historical Populations.- Chapter 11 Estimating Life Expectancy.- Chapter 12 Stable Population Theory.- Chapter 13 Decomposition of Cohort Change Ratios.- Chapter 14 Forecasting Incorporating Spatial Dependencies.- Chapter 15 The Utility of Cohort Change Ratios.- Chapter 16 Concluding Remarks.- Subject Index.
1 Introduction
1(12)
1.1 Why a Book on Cohort Change Ratios?
1(1)
1.2 Cohorts and Their Analyses
2(1)
1.3 The Cohort Change Ratio
2(4)
1.4 Reverse CCRs
6(1)
1.5 Census Survival Ratios
7(1)
1.6 Some Applications of Cohort Change Ratios
7(1)
1.7 About This Book
8(5)
References
10(3)
2 Basic Demographic Concepts
13(22)
2.1 Introduction
13(1)
2.2 Estimates, Projections, and Forecasts
13(1)
2.3 Demographic Concepts
14(10)
2.3.1 Size
14(1)
2.3.2 Distribution
15(2)
2.3.3 Composition
17(2)
2.3.4 Change
19(5)
2.4 Statistical Measures
24(6)
2.4.1 Ratios
24(2)
2.4.2 Rates and Probabilities
26(3)
2.4.3 The Odds Ratio
29(1)
2.5 Participation-Rate Method
30(5)
2.5.1 Logic and Formulas
30(1)
2.5.2 Implementation Issues
31(1)
References
31(4)
3 Sources of Demographic Information
35(10)
3.1 Introduction
35(1)
3.2 United States Census Bureau
36(1)
3.3 Decennial Census
36(3)
3.4 Population Estimates
39(1)
3.5 Surveys
39(2)
3.6 Administrative Records
41(1)
3.7 International Data
42(1)
3.8 Other Data Sources
42(1)
3.9 On-line Location of Excel Files
43(1)
3.10 Conclusions
44(1)
References
44(1)
4 Forecasting Population Size and Composition
45(14)
4.1 Introduction
45(1)
4.2 Hamilton-Perry Forecast
46(6)
4.2.1 Forecast by Age and Gender
46(2)
4.2.2 Forecast by Age
48(1)
4.2.3 Forecast of Major League Pitchers
49(3)
4.3 Controlling a Hamilton-Perry Forecast
52(4)
4.4 Conclusions
56(3)
References
57(2)
5 Forecasting Using Modified Cohort Change Ratios
59(24)
5.1 Introduction
59(1)
5.2 Modifying Cohort Change and Child-Woman Ratios
60(2)
5.3 Measures of Forecast Error
62(1)
5.4 Empirical Data
63(2)
5.5 Empirical Results
65(15)
5.5.1 Total Population Forecast Error
65(2)
5.5.2 Forecast Error by Age Group
67(7)
5.5.3 Total Population Forecast Error by Population Size and Growth Rate
74(6)
5.6 Conclusions
80(3)
References
82(1)
6 Forecasting Uncertainty
83(24)
6.1 Introduction
83(1)
6.2 Forecast Uncertainty
83(1)
6.3 Statistical Forecast Intervals
84(5)
6.3.1 Model-Based Intervals
84(2)
6.3.2 Empirically-Based Intervals
86(3)
6.4 Statistical Intervals for Cohort Change Ratios and Population Forecasts
89(5)
6.4.1 Statistical Inference and the Concept of a Super-Population
89(1)
6.4.2 Hamilton-Perry Method
89(2)
6.4.3 Incorporating Uncertainty into the Hamilton-Perry Method
91(3)
6.5 Evaluation
94(4)
6.5.1 Age Groups
95(2)
6.5.2 Total Population
97(1)
6.6 Conclusions
98(9)
Appendix
100(1)
Cohort Change Ratios and the Fundamental Demographic Equation
100(2)
References
102(5)
7 Forecasting School Enrollment Size and Composition
107(12)
7.1 Introduction
107(1)
7.2 Short-Term Enrollment Forecasting by Grade
108(2)
7.3 Long-Term Student Population and Enrollment Forecasting by Grade
110(5)
7.4 Evaluation
115(2)
7.5 Conclusions
117(2)
References
117(2)
8 Forecasting Other Characteristics
119(24)
8.1 Introduction
119(1)
8.2 Studies Using the Participation-Rate Method
120(3)
8.2.1 Disability in the United States
120(1)
8.2.2 Obesity in the United States
120(1)
8.2.3 Cardiovascular Disease in the United States
121(2)
8.3 Developing Population-Related Forecasts
123(14)
8.3.1 Alcohol Consumption in the United States
123(2)
8.3.2 Diabetes in the United States
125(3)
8.3.3 Cigarette Use and Consumption in the United States
128(3)
8.3.4 Civilian Labor Force Forecast for San Diego County, California
131(3)
8.3.5 Other Population and Housing Variables for San Diego County, California
134(3)
8.4 Conclusions
137(6)
References
139(4)
9 Estimating Population Size and Composition
143(8)
9.1 Introduction
143(1)
9.2 Interpolation Methods
144(1)
9.3 Examples
145(5)
9.4 Conclusions
150(1)
References
150(1)
10 Estimating Historical Populations
151(14)
10.1 Introduction
151(1)
10.2 Reverse Cohort Change Ratios
151(2)
10.3 Examples
153(9)
10.3.1 1910 Native Hawaiian Population Estimates in Hawai'i
153(1)
10.3.2 1770 to 1900 Native Hawaiian Population Estimates in Hawai'i
153(6)
10.3.3 CCRs and Life Table Survival Rates
159(1)
10.3.4 Multi-racial Population Estimates for San Bernardino and Riverside Counties
160(2)
10.4 Conclusions
162(3)
References
163(2)
11 Estimating Life Expectancy
165(8)
11.1 Introduction
165(1)
11.2 Estimating Life Expectancy
165(1)
11.3 Life Expectancy: The United Nations Census Survival Method
166(1)
11.4 Estimating Life Expectancy from Cohort Change Ratios
167(1)
11.5 Empirical Examples and Evaluation
168(2)
11.6 Conclusions
170(3)
Appendix
170(1)
Relation Between Survival Rates and Life Expectancy
170(1)
References
171(2)
12 Stable Population Theory
173(18)
12.1 Introduction
173(1)
12.2 Cohort Change Ratios and the Stable Population Model
174(1)
12.3 Illustration of Stable Populations with and without Migration
175(4)
12.4 Impact of Demographic Components of Change on Convergence
179(3)
12.5 Other Strategies to Analyze Convergence
182(5)
12.5.1 Clarifying Measures of Convergence: Transient or Asymptotic Dynamics
182(1)
12.5.2 Components of Change: Interactions and Convergence
183(3)
12.5.3 Perturbation Analysis and the Life Table Response Experiment Framework
186(1)
12.6 Conclusions
187(4)
References
187(4)
13 Decompositions
191(18)
13.1 Introduction
191(1)
13.2 Decompositions
192(3)
13.2.1 Subgroup Decomposition
192(1)
13.2.2 Components of Change Decomposition
193(1)
13.2.3 Subgroup and the Components of Change Decomposition
194(1)
13.3 Applications
195(11)
13.3.1 Contribution of Subgroup CCRs to the Total CCR
195(2)
13.3.2 Indirect Forecasts of the Components of Change
197(6)
13.3.3 Contribution of Subgroup Components of Change to Total Population Change
203(3)
13.4 Conclusions
206(3)
References
207(2)
14 Forecasting with Spatial Dependencies
209(16)
14.1 Introduction
209(1)
14.2 Issues with Georeferenced Data
210(1)
14.3 Modeling Spatial Dependencies: Spatial Weights Matrices
210(5)
14.3.1 Denning a Geographic Neighborhood
211(2)
14.3.2 Constructing and Using a Spatial Weights Matrix
213(2)
14.4 Spatially-Weighted Hamilton-Perry Forecast
215(1)
14.5 Alternative Spatial Approaches
216(3)
14.6 Boundary Changes
219(1)
14.7 Conclusions
220(5)
References
220(5)
15 The Utility of Cohort Change Ratios
225(22)
15.1 Introduction
225(1)
15.2 The Concept of Utility
225(2)
15.3 Utility and the Cohort Change Ratio Method
227(9)
15.4 Conclusions
236(11)
References
245(2)
16 Concluding Remarks
247(6)
16.1 Introduction
247(1)
16.2 Top Ten Reasons to Use the CCR Method
248(5)
References
250(1)
Appendix
251(1)
Cohort Change Ratios and the Fundamental Demographic Equation...
251(2)
Index 253
Jack Baker is a Senior Research Analyst with the HealthFitness Corporation, a Wellness and Health Promotion company based in Lake Forest, Illinois. He holds a Ph.D. in Anthropology from the University of New Mexico. In the applied demography world, Jack worked within the Federal State Cooperative Programs for Population Estimates and Projections for over a decade, served on the National Academies of Sciences Panel to Review the 2010 Census (2009-2014) and its follow-up Standing Committee on Re-engineering the 2020 Census, and previously chaired the Population Association of Americas Committee on Applied Demography. His research has spanned estimates and forecasts, with an emphasis on small-area models of population dynamics, and his work has appeared in various peer-reviewed journals. His current research focuses on the construction of integrated modelling frameworks spanning individual and population scales, with an emphasis on predictive analytics and forecasting applications inthe context of population health dynamics. His work makes use of methods from applied demography, statistical/machine learning, operations research, and biostatistics. 

David A. Swanson is Professor of Sociology at the University of California Riverside and an affiliate of the Demographic and Social Analysis Program at the University of California Irvine. He served as member of the U. S. Census Bureaus Scientific Advisory Committee for six years (2004-10) and chaired the group for two years (2008-10). In addition to many presentations, reports, and 30 non-refereed journal articles, Swanson has authored or co-authored over 90 refereed journal articles and 13 books (some as an editor or co-editor), mainly dealing with demography, especially methods for doing small area estimation and forecasting. Swanson has received more than $2.3 million in grants and contracts and has testified before the US Congress and state legislative bodies. He also has served as an expert witnessin court cases and is the recipient of two Fulbright awards. With Jeff Tayman as co-author, he received the Southern Demographic Associations 2016 Terrie Award for the best paper in applied demography and with Tayman and Charlie Barr received this same award in 1999. His Ph.D. and M.A. are from the University of Hawaii (Sociology, with a concentration in Population Studies) and his B.Sc. is from Western Washington University (with a major in Sociology and a minor in Mathematics). Swanson also holds a graduate diploma in Social Sciences from the University of Stockholm and is reasonably fluent in Swedish.



 Jeff Tayman is a specialist in the evaluation of demographic estimates and forecasts; the development of estimation and forecasting methods; the design and application of statistical methods; and the development of complex, integrated data systems for small geographic areas. He has made 90 presentations at professional conferences and has published 27 articles in referred journals. His three co-authored books State and Local Population Projections: Methodology and Analysis, A Practitioners Guide to State and Local Population Projections, and Subnational Population Estimates are standards in the field. Dr. Tayman has served on numerous federal, California, and local committees and is a reviewer for academic journals including Demography and Population and Research and Policy Review. He currently holds a part time appointment in the Economics Department at the University of California San Diego. His Ph.D. and M.S. degrees are from Florida State University (Sociology with a concentration in Demography and minor in Statistics) and B.A. degree is from Florida Atlantic University (with a major in Anthropology).

 Lucky M. Tedrow is the Director, Demographic Research Laboratory, Center for Social Science Instruction, Western Washington University. His research areas span applied and social demography. Research interests include antecedentsand outcomes of military service. Computer applications and undergraduate instruction are main areas of focus. He has been the principal investigator on seven National Science Foundation-funded grants for undergraduate education. 





 
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