General Information
Objective
Background
Main literature
Timetable
Lectures
Assignments
Evaluation
Literature:

Books/reports on reserve in library
Highly relevant articles (including required reading), on reserve in library
List of literature (for information only)

General Information
 

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Objective

At the end of the course, students should be able to calculate multistate life tables for different data types, including census data, vital statistics, and survey data. They should be able to (re)construct cohort biographies and individual biographies from incomplete data, while explicating the assumptions that need to be made in the absence of comprehensive empirical evidence. They should be able to make multistate population projections and forecasts, and assess the long-term consequences of various demographic regimes. They should also be able to apply simple policy models to determine the interventions that are required to influence the population composition (e.g. marital status composition, household types, number of persons unemployed, number of diseased and disabled, number of residents in given set of regions) and the sojourn times in various stages of life (e.g. expected duration of unemployment, expected duration of disease and disability, expected length of time spent without social support).

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Background

Multistate demography is about the modeling, analysis and projection of populations  stratified by age, sex, and one or several attributes, e.g. health status, region of residence, employment status, household status. At each age, a person is characterized by a set of attributes. Some attributes are used as stratification variables (primary attributes). Age is generally viewed as a stratification variable, but in life history analysis, it is considered as a duration variable. Other attributes that are relevant but are not used to stratify the population, are secondary attributes or covariates. In multistate analysis, people that share common primary attributes are said to occupy the same state; they define a subpopulation. People with different attributes occupy different states (and belong therefore to different subpopulations). The states are discrete, hence an individual cannot occupy more than one state at a time, as e.g. in the grade-of-membership model. State occupancy is a central concept of multistate demography. The number of states distinguished is the state space. In a multistate population, individuals are allowed to move from one state to another. That results in interstate flows and the similarity between multistate models and flow models, e.g. in labour market research and geography. Some states may be entered and left freely (transient states). Other states can be entered but not left (absorbing states). The state occupancy is recorded at a point in time. We are often also interested in how long people stay in a given state before moving on to another state. It is the sojourn time in a state, or the length of an episode or stage.

A change in primary attribute implies a transition from one state to another and the end of an episode. State transition is a central concept. State transitions have two important consequences:

1. For the life course: transitions affect the time people spend in each episode of life (duration; sojourn time).
2. For the population: the transitions people experience, change the composition of the population. That is the basic link between micro (individual life histories) and macro (population change): experiences at the individual level have consequences at the population level. A transition at the individual level results in an attrition from one subpopulation (decrement, exit) and an addition to another subpopulation (increment, entry).

In order to keep track of the impact of individual transitions on the population, extensive demographic accounting schemes are used, either independently or as part of a multistate demographic model. The impact of the transitions on a stationary population is described by the multistate life table. The impact on a population that grows or declines at a constant rate (dynamic equilibrium) is given by the multistate stable population theory. The impact of transitions on a real population is given by multistate population projection models. The multistate life table is also referred to as an increment-decrement life table, in particular when it is approached as an extension of the multiple decrement life table which allows attrition for a number of reasons (e.g. causes of death; reasons for leaving home). In short, multistate demography studies the dynamics of multistate populations, i.e. populations stratified by a set of attributes. Multistate populations have also been referred to as multigroup populations (Schoen, 1988). Multistate demography has also been referred to as multidimensional demography (Land and Rogers, 1982). The founding father of multistate demography, Andrei Rogers, considered a population stratified by region of residence and defined the field as multiregional demography (Rogers, 1975, 1995). The term ‘multidimensional demography’ is also used (e.g. Land and Rogers, 1982).

Often, cohorts are distinguished (groups of people that share some initial condition, such as year and place of birth, or year and place of entry into graduate education). Cohorts can be real or hypothetical (synthetic). Traditionally, multistate demography is applied to cohorts. Cohort dynamics describes the life course of people born in the same period (e.g. year, five-year period). Period analysis studies the changes in a population from one point in time to another; it views the population as being composed of different cohorts (e.g. multistate cohort-survival model). The common life path experienced by the members of the cohort, has been referred to as the cohort biography or macro-biography (Ryder, 1965). It is the sequence of states cohort members occupy as they age. Intra-cohort variation received little attention initially, but is currently an important subject of research. The study of intra-cohort variation considers the effects of secondary attributes on transitions. Cohort members may have different initial conditions (e.g. endowments) and different experiences. Consequently, cohort biographies represent averages and there may be considerable differences in the biographies of individual members of a cohort. Intra-cohort variation emphasizes the differences in individual life histories of cohort members. Inter-cohort variation focuses on the differences in average biographies of cohort members. In general, the study of individual life histories requires individual data and repeated measurements (longitudinal data). The models are statistical specifications of multistate models. An extensive review is given by Hougaard (1999, 2000). Repeated observations on the same individuals are not commonly available. Sometimes, intra-cohort variation can be studied in the absence of observations on individuals; namely, when we know the distribution of a given attribute in a cohort; i.e. when we know the density function of a given attribute. This approach is adopted in micro-simulation and the study of unobserved heterogeneity. The approach is useful when individual data on a given attribute are lacking but when the distribution of the attribute in the population (cohort) is known/given.

The course approaches multistate demography from the perspective of cohort biographies and individual biographies. Examples of biographies or life histories are:

• fertility histories; fertility career
• employment histories; employment career
• migration histories; migration career
• histories of living arrangement
• health histories

1. (re)Construction of life histories from available (frequently incomplete) data on transition rates and transition probabilities (from vital statistics or from surveys). The (re)construction relies on the multistate life table and extensions, Traditionally, multistate life tables describe life histories of cohorts (‘macro-biographies’). Cohorts may be real, but are often synthetic. Members of a same cohort may differ greatly and the heterogeneity may lead to selection that change the composition of the population. In the course, intra-cohort variation is also considered, using logit and log-rate models for transition data. Participants will learn how to construct multistate life tables using Excel and the specialized packages SPACE and LIFEHIST.

2. Multistate stable population theory. Stable population theory is used for two main reasons:
   1.    To explore the relation between individual life histories and population characteristics (see e.g. Preston, 1982).
   2.    To explore the consequences of current life histories and variations in life histories. Stable populations are  populations in steady-state equilibrium. Stable population theory is about the ultimate (asymptotic) characteristics of a population that experiences particular demographic regimes (transitions) over a long period. The theory is used to magnify the effects of a current demographic regime and to assess the consequences of small changes in demographic behaviour (microscopic view). 

Applications of stable population theory to real data is enhanced  by packages such as SPACE.

3. Multistate demographic projection models. In multistate projection models, several subpopulations are considered simultaneously and the effects of transactions (transitions) among subpopulations on the future population composition are quantified. As part of the teaching of projection models, two software packages will be presented: MUDEA and LIPRO. LIPRO 4.0 (Windows version) will be used more extensively.
4. Multistate population policy analysis. Policy models are developed to determine the interventions that are required to influence population dynamics. Policies may be targeted at particular subpopulations, e.g. particular household type, the unemployed, the elderly, young adults, the diseased and disabled, inhabitants of a given region, etc. They may also address the structure of the population, e.g the age structure or the spatial distribution. Policies may also be designed to influence the life course of people, in particular the sojourn times in various stages of life (e.g. expected duration of unemployment, expected duration of disease and disability, expected length of time spent without social support).

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Main literature

Literature (in general, with focus on methods): suggested reading

• Rogers, A. (1975) Introduction to multiregional mathematical demography. Wiley, New York.
• Rogers, A. (1995) Multiregional demography. Principles, methods and extensions. Wiley, Chichester. (With diskette).
  • Chapter 2 Spatial population dynamics
  • Chapter 4 The multiregional life table
  • Chapter 5 Multiregional projection and stable growth
• Schoen, R. (1988) Multigroup populations. Plenum Press, New York
  • Chapter 4 The multistate life table
  • Chapter 5 The multistate stable population
• Willekens, F.J. and A. Rogers (1978) Spatial population analysis. Methods and computer programs. IIASA, Research Report RR-78-18.
• Rogers, A. and F.J. Willekens (1986). A short course on multiregional mathematical demography.
• Willekens, F. (1998) Demographic projection models. A technical introduction. Manuscript.
• Journal articles

Timetable

Wednesday 14.00 - 17.00 p.m.

Thursday 9.00 - 12.00 a.m.

NOTE: the applications take a seminar format (relatively brief presentation and extensive discussion; be prepared!)

Day	Date	Time	Location	Subject
Wednesday	29 May	14-17	WSN 64	1. Introduction to multistate demography
				2. Elements of probability theory: state occupancies and theory of competing risks
Thursday	30 May	9-12	WSN64	3. Elements of matrix algebra
				4. Multistate models (transition model)
Wednesday	5 June	14-17	WSN64	4. Multistate models (transition model)
				5. Life tables of staging processes: the fertility table
Thursday	6 June	9-12	WSN64	6. The multistate life table
Wednesday	12 June	14-17	WSN64	7. Application 1. Multistate models in health research: public health
				1A. Public Health: Multistate life-table analysis of the Framingham Hearth Study (by A.A. Mamun)
				1B: Reproductive health: Contraceptive use dynamics using calendar data and multistate life tables (by Mouri Khatun)
Thursday	13 June	9-12	WSN64	8. The multistate life table and regression models
				9. Selected topics in multistate life tables
Wednesday	19 June	14-17	WSN64	10. Application 2. Multistate life tables of marital and fertility careers
				2A. Changing marriage and fertility during the Second Demographic Transition: the case of Japan and The Netherlands (by Hideko Matsuo)
				2B. Changing reproductive lives of women in India (by Sabu Padmadas)
Thursday	20 June	14-17	WSN64	11. Multistate stable population theory
Wednesday	26 June	9-12	WSN64	12. Multistate population forecasting
Thursday	27 June	14-17	WSN64	13. Questions and answers, including discussion of final assignment (synthetic biographies)
Thursday	4 July	9-12	t.b.a.	Exam

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Lectures

1.    Introduction to multistate demography and transition data analysis

1. Synthetic biographies: reconstruction of life histories (macro-biographies of cohorts, and individual biographies).

• TRANSITION DATA ANALYSIS: event-based analysis (transition data = multiple-spell data = multi-episode data = event data)
• MULTISTATE ANALYSIS: status-based analysis illustrated using the Lexis diagram

2. Data types and measurement issues

• Sources of life history data
• Longitudinal data: retrospective and prospective measurement of life histories
• Life history data structures; The life history calendar (Freedman et al.)
• Issues in the measurement of life histories:
  • observation window: observations are limited to segments of life
  • observational plans (Lexis diagram)
    • Status data vs. event data (transition data vs. movement data)
    • Period versus cohort
  • nonresponse, memory lapse, misreporting (e.g. heaping), missing data
• The following life history data are available for analysis:

1. Small data sets

• Micro data: selection of German Life History Study (GLHS), employment histories of 201 respondents (Blossfeld and Rohwer, 1995)
• Tabulated data: marital status change, employment status change, migration data (e.g. Census USA), etc.

2. Full data sets

• National Family Health Survey 1992-94 (NFHS), India
• Family and Fertility Survey 1993, 1998 (OG), The Netherlands
• Bangladesh Demographic and Health Survey 1996-97 (BDHS)

• Willekens (1999c) in Van Wissen and Dykstra)
• Blossfeld and Rohwer (1995)
  • Chapter 1: Introduction.
  • Chapter 2: Event history data structures
• History of multistate demography: Willekens (1985)
• Data types in multistate analysis: Rees and Willekens (1986)

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2.    Elements of probability theory: state occupancies and theory of competing risks (for details, see course Life History Data Analysis, available as PowerPoint presentation)

1. Descriptive statistics: odds, odds ratios, logits, relative risks
2. Introduction to probability theory: random variables, density function, distribution functions, quantile functions, hazard functions, link functions
3. Basic probability distributions and regression models for multistate demography:

1. Continuous random variables

i.    The uniform distribution
ii.    The exponential distribution and the exponential model

2. Discrete random variables

i. The Bernoulli distribution and the binomial and multinomial distributions
ii. The logit model and the logistic regression model
iii.The Poisson distribution, the log-linear model and the log-rate model

3. Stochastic process

Reading:

• Probability theory: any introductory text. A good text is Taylor and Karlin (1994)
• Competing risks:
  • Namboodiri (1990, Chapter 4 ‘Multiple-decrement tables’)
  • Hachen (1988)
  • Blossfeld and Rohwer (1995, Chapter 6)

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3.    Elements of matrix algebra

Reading:    any text (see e.g. Namboodiri, 1984; a very good text is Rogers, 1971)

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4.    Multistate models

1. State occupancy depends on state occupied at previous point in time: the Markov model derived from the (multinomial) logit model
2. The transition model for status data (Option 2; panel data)
3. The transition model for event data (number of events or rates)

i. The accounting equation and the rate equation
ii. The ‘linear’ model
iii. The ‘exponential’ model

4. Events from status data: the inverse method for estimating transition rates from transition probabilities
5. Multistate policy models
6. Statistical inference for multistate models: maximum likelihood
7. Estimation of transition models using SPSS and TDA

Reading:

• Willekens (1998)
• Rogers (1995, Chapter 2)
• Policy models:
  • Rogers (1971, pp. 98-108)
  • Willekens and Rogers (1977)
  • Rogers (1990)
• Statistical inference:
  • Hougaard (1999)
  • Strauss and Shavelle (1998): Kaplan-Meier estimator for multistate life tables
  • Gill and Keilman (1990)
• Other relevant reference: Policy models:
  • Willekens (1979)
  • Tan and Bennett (1984)

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5.    Life tables for staging processes

1. The fertility table of Chiang and van den Berg
2. The disease model of Barendregt et al.

Reading:

• Chiang (1984, Chapter 12)
• Barendregt et al. (1998)

• Hougaard (2000, pp. 155ff: ‘Progressive models’)

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6.    The multistate life stable

1. The multistate life table from vital statistics and census data

• Option 1 and option 2
• Estimation of transition probabilities and transition rates

2. The multistate life table from survey data (micro-data)
3. Software: SPACE, LIFEHIST

Reading:

• Schoen (1988, Chapter 4)
• Rogers (1995, Chapter 4)
• Namboodiri and Suchindran (1987, Chapter 9)
• Willekens and Rogers (1978)
• Rogers and Willekens (1986)

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7.    Application 1: Multistate models in health research

1. Public health

Multistate life-table analysis of the Framingham Hearth Study (by A.A. Mamun)

2. Reproductive health

Contraceptive use dynamics using calendar data and multistate life tables (by Mouri Khatun)

Reading:

• Mamun (2000)
• Rogers et al. (1989)
• Crimmins et al., (1994)
• Mathers and Robine (1997)
• Newman (1988)
• Khatun and Willekens (2001)

• Islam (1994)
• Biritwum and Odooms (1995)

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8.    The multistate life table and regression models

a. The logit model (probabilities)
b. The log-rate model (rates)
c. Extended multistate models (Markov extension models)

i. Semi-Markov model: hazard depends on duration in current state
ii. Extended transition model

• Gill (1992)
• Gentleman et al. (1994)
• Hannan (1984)
• Richards et al. (1987)
• Rajulton and Lee (1988)
• Wolf (1988)
• Lawless and Fong (1999)
• Other relevant reference: Tuma and Hannan (1984)

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9.    Selected topics in multistate life tables

1. Multiple contingency calculations: multistate life tables for insurance
2. Episodes of disability and severity of disability: disability-adjusted years of life (DALYs)
3. Multistate choice models (discrete choice)
4. Multistate models for disease histories in AIDS research
5. Estimation of confidence intervals for multistate life tables

Reading:

• Keyfitz and Rogers (1982)
• Murthi and Srinivasan (1999)
• Gentleman et al. (1994)
• Other relevant reading material: Schettkat (1996), Devine and Kiefer (1991)

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10.    Application 2. Multistate life tables of marital and fertility careers and multiregional life tables

1. Changing marriage and fertility during the Second Demographic Transition: the case of Japan and the Netherlands (by Hideko Matsuo)
2. Changing reproductive lives of w omen in India (by Sabu Padmadas)

Reading: to be determined

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11.    Multistate stable population theory

a. Introduction to stable population theory

• Asymptotic behaviour of multistate models, including the Markov model
• Age-structured populations:
  • The continuous-time model: the Lotka equation
  • The discrete-time model: the Leslie model
  • Relations under stability

• Introduction
• The spatial reproductive value
• Application: the momentum of zero population growth

• Willekens and Rogers (1978)
• Rogers (1995, Chapter 5)
• Schoen (1988, Chapter 5)
• Preston (1982)
• Willekens (1993)

• Rogers (1975)
• Willekens and Philipov (1981)

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12.    Multistate population forecasting

a. The demographic projection model

• The exponential model
• The geometric model

Reading:

• Willekens (1998)
• Rogers (1995, Chapter 5)
• Medina (2000, Chapter 3)
• Van Imhoff (1990)
• Zeng Yi et al. (1997)

• Rogers (1985)
• Rees and Willekens (1989)

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13.    Questions and answers

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Assignments (tentative)

• (29 May, to be submitted 5 June) Prepare a data base for multistate analysis: origin-destination ‘accounts’; event data and transition data from retrospective life history (National Family Health Survey (India) and Netherlands Family and Fertility Survey 1993 and 1998 (and/or other surveys if students prefer and data are available)). Use SPSS or SPlus, and the new programme MLTA_MICRO (multistate life table analysis from micro data).
• (5 June, to be submitted 12 June) Transition models: estimate a transition model (without age detail) and study the properties of the model using matrix techniques.
• (12 June, to be submitted 19 June)

1. Use the data prepared in the first assignment to construct a multistate life table (non-parametric method) 
2. Search the literature for applications of the multistate life table (demography; public health (e.g. health expectancy); sociology; economics (labour market economics; market research); geography; political science (e.g. voting behaviour).

• (19 June, to be submitted 4 July) Synthetic biographies: Reconstruct cohort biographies and individual life histories using multistate life tables with covariates. Project the cohort biographies into the future using LIPRO or MUDEA.

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Evaluation

• Assignments (70 %). The assignments are essential; they cover the essentials of what you need to know. In principle, there is one assignment each week. Assignments must be completed within a week. They will be discussed in class. The final assignment is larger and covers the entire course.
• Written exam (30 %)

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Literature: Books/reports on reserve in library

• Rogers, A. (1995) Multiregional demography: principles, methods and extensions. Wiley, Chichester. ISBN 0-471-95892-1
• Rogers, A. and F. Willekens (1986) Migration and settlement: a multiregional comparative analysis. Reidel Publishing, Dordrecht, The Netherlands. ISBN 90-277-2119-X
• Schoen, R. (1988) Modeling multigroup populations. Plenum, New York. ISBN 0-306-42649-8
• Willekens, F. (1985) Multiregional demography. Working Paper no. 59, NIDI, Voorburg (The Hague)

Literature: Highly relevant articles (including required reading)
These articles are on reserve in the library

• Barendregt,  J.J., G.J. van Oortmarssen, B.A. van Hout, J.M. van den Bosch and L. Bonneux (1998) Coping with multiple morbidity in a life table. Mathematical Population Studies, 7(1):29-49 Also published in : J.J. Barendregt and L. Bonneux (1998) Degenerative disease in an aging population. Models and conjectures. PhD dissertation, Erasmus University, Chapter 12. (follows)
• Broersma, L., F.A.G. den Butter and U. Kock (2000) A national accounting system for worker flows. Economics Letters, 67:331-336
• Chiang, C.L. (1984) The life table and its applications. R.E. Krieger Publishing Company, Malabar, Florida, Chapter 12
• Crimmins,E.M., M.D. Hayward, and Y. Saito (1994) Changing mortality and morbidity rates and the health status and life expectancy of the older population. Demography, 31(1), pp. 159-75.
• Espenshade, T.J. (1987) Marital careers of American women: a cohort life table analysis. In: J. Bongaarts, T. Burch and K. Wachter eds. Family demography: methods and their applications. Clarendon Press, Oxford, pp. 150-167.
• Gentleman, R.C., K.F. Lawless, J.C. Lindsey and P. Yan (1994) Multi-state Markov models for analysing incomplete disease history data with illustrations for HIV disease. Statistics in Medicine, 13(8):805-821.
• Gill, R.D. (1992) Multistate life tables and regression models. Mathematical Population Studies, 3(4):259-276
• Gill, R.D. and N. Keilman (1990) On the estimation of multidimensional demographic models with population registration data. Mathematical Population Studies, 2(?):119-143.
• Hachen, D.S. (1988) The competing risk model. Sociological Methods and Research, 17(1):21-54. Reprinted in in D.J. Bogue, E.E. Arriaga and D.L. Anderton eds. Readings in population research methodology. Social Development Center, Chicago, and UNFPA, New York, pp. 21.85-21.101. (see Life History Data Analysis)
• Hannan, M.T. (1984) Multistate demography and event history analysis. In: A. Diekmann and P. Mitter eds. Stochastic modeling of social processes. Academic Press, Orlando, pp. 39-88.
• Hayward, M.D., C. Hsinmu and E.M. Crimmins (??) Evaluating group differences in healthy life expectancy: the estimation of confidence intervals for multistate life table expectancies. Manuscript.
• Hougaard, P. (1999) Multi-state models: a review. Lifetime Data Analysis, 5(3):239-264.
• Keyfitz, N. and A. Rogers (1982) Simplified multiple contingency calculations. Journal of Risk and Insurance, 49:59-72.
• Kuo, H.S., H.J. Chang, P. Chou, L. Teng and R.H.H. Chen (1999) A Markov chain model to assess the efficacy of screening for non-insulin dependent diabetes mellitus (NIDDM), International Journal of Epidemiology, 28:233-240.
• Lawless, J.F. and D.Y.T. Fong (1999) State duration models in clinical and observational studies. Statistics in Medicine, 18:2365-2376
• Mamun, A.A. (2000) Multistate models in public health. A review and an application to the Framingham Hearth Study. Masters Thesis Demography, Population Research Centre, Faculty of Spatial Sciences, University of Groningen
• Mathers, C.D. and J.M. Robine (1997) How good is Sullivan's method for monitoring changes in population health expectancies? Journal of Epidemiology and Community Health, 51(1): 80-86. (Follows)
• Medina, S. (2000) Human resources and population in Mexico at the dawn of the twenty-first century. Thela Thesis Publishers, Amsterdam (PhD dissertation, University of Groningen), Chapter  3 (pp. 57-70) and pp. 114-125
• Namboodiri, K. (1990) Demographic analysis. A stochastic approach. Academic Press, San Diego. Chapter 4.
• Namboodiri, K. and C.M. Suchindran (1987) Life table techniques and their applications. Academic Press, Orlando. Chapter 9
• Newman, S.C. (1988) A Markov process interpretation of Sullivan’s index of morbidity and mortality. Statistics in Medicine, 7:787 - 794
• Rajulton, F. (1992b) Life history analysis: guidelines for using the program LIFEHIST (PC version). Discussion Paper no. 92-5, Population Centre, University of Western Ontario, London, Ontario, Canada
• Rajulton, F. and H.Y. Lee (1988) A semi-Markov approach to using event-history data in multiregional demography. Mathematical Population Studies, 1:289-315.
• Rees, Ph. And F.J. Willekens (1986) Data and Accounts. In: A. Rogers and F. Willekens eds. Migration and settlement: a multiregional comparative study. Dordrecht, The Netherlands: Reidel Press, pp. 19-58
• Rees, Ph. and F.J. Willekens (1989) Population projections: Dutch and English multiregional methods. In: J. Stillwell and H.J. Scholten eds. Contemporary research in population geography. Kluwer Academic Publishers, Dordrecht, pp. 19-37
• Rogers, A. (1971) Matrix methods in urban and regional analysis. Holden-Day, San Francisco, pp. 98 – 108
• Rogers, A. (1985) Regional population projection models. Sage Publications, Beverly Hills.
• Rogers, A. (1990) The multistate stable population model with immigration. Mathematical Population Studies, 2(4):313-324.
• Rogers, A. (1995) Multiregional demography. Principles, methods and extensions. Wiley, Chichester. Chapter 2, Chapter 4, Chapter 5 (book on reserve in library)
• Rogers, A. and F.J. Willekens (1986) A short course on multiregional. In: A. Rogers and F. Willekens eds. Migration and settlement: a multiregional comparative study. Dordrecht, The Netherlands: Reidel Press, pp. 355-384.
• Rogers, A., R.G. Rogers and L.C. Branch (1989) A multistate analysis of active life expectancy. Public Health Reports, 104(3):222-226.
• Scherbov, S. and H.A.W. van Vianen (1999) Marital and fertility careers of Russian women born between 1910 and 1935,  Population and Development Review, 25(1):129-143
• Schoen, R. (1988a) Modeling multigroup populations. Plenum Press, New York. Chapter 2, Chapter 4
• Van Imhoff, E. (1990) The exponential multidimensional demographic projection model. Mathematical Population Studies, 2(3):171-182.
• Willekens, F.J. (1980) Multistate analysis of tables of working life, Environment and Planning A, 12:563-588. Reprinted in: D.J. Bogue, E.E. Arriaga and D.L. Anderton eds. Readings in population research methodology. Social Development Center, Chicago, and UNFPA, New York, 1993, pp. pp. 22.13 - 22.38.   (NIDI Reprint)
• Willekens, F.J. (1985) Multiregional demography. Working Paper no. 59, Voorburg: NIDI (on reserve in library)
• Willekens, F.J. (1987) The marital status life-table. In: J. Bongaarts, T. Burch and K.W. Wachter eds. Family demography: models and applications. Oxford: Clarendon Press (for IUSSP), pp. 125-149.
• Willekens, F.J. (1977) Sensitivity analysis in multiregional demographic models. Environment and Planning A, 9:653-674
• Willekens, F.J. (1993) The impact of fertility change on population distribution. In: N. van Nimwegen, J.C. Chesnais and P. Dykstra eds. Coping with sustained low fertility in France and the Netherlands. Swets and Zeitlinger, Amsterdam, pp. 161-193.
• Willekens, F.J. (1995) MUDEA. Version 2.0. Manual and tutorial. Report. Faculty of Spatial Sciences, University of Groningen.
• Willekens, F.J. (1998) Demographic projection models. A technical introduction. Manuscript.
• Willekens, F.J. (1999b) Modeling approaches to the indirect estimation of migration flows: from entropy to EM. Mathematical Population Studies, 7(3):239-278
• Willekens, F.J. (1999c) The life course: models and analysis. In: L.J.G. van Wissen and P.A. Dykstra eds. Population issues: an interdisciplinary focus. Plenum Press, New York, pp. 23-51.
• Willekens, F.J. and P. Drewe (1984) A Multiregional model for regional demographic projection. In: H. ter Heide and F. Willekens eds. Demographic research and spatial policy. London: Academic Press, 1984, pp. 309-334. Reprinted in: D.J. Bogue, E.E. Arriaga and D.L. Anderton eds. Readings in population research methodology. Social Development Center, Chicago, and UNFPA, New York, 1993. ) pp. 17.49 - 17.57.
• Willekens, F.J. and A. Rogers (1978) Spatial population analysis. Methods and computer programs. IIASA, Research Report RR-78-18.
• Willekens, F.J. and D. Philipov  (1981) Dynamics of multiregional population systems: a mathematical analysis of the growth path. Working Paper no. 22, Voorburg: NIDI, 1981.
• Wolf, D.A. (1988) The multistate life table with duration dependence. Mathematical Population Studies, 1(3):217-245.
• Zeng Yi, J.W. Vaupel and W. Zhenglian (1997) A multidimensional model for projecting family households. With an illustrative numerical application. Mathematical Population Studies, 6(3):187-216

List of literature (for information only)

Note: At the RUG, you may have electronic access to many journals.
See: http://www.bibliotheek-ebr.rug.nl/ee-media3.html and
http://www.ub.rug.nl/bib/ej.html

Note: Many of the most significant papers in population research methodology are included in the Readings in Population Research Methodology (8 volumes), eded by D. Bogue et al., and published in 1993 for the United Nations Population Fund by the Social Development Center, Chicago, Ill. Of particular relevance is Chapter 22: Multistate methods (Volume 6, pp. 22.1-22.109)

Introduction to multistate mathematical demography (Rogers)
Multistate analysis: tables of working life (Willekens)
Multidimensional analysis with incomplete data (Willekens)
Parameterized multistate population dynamics and projections (Rogers)
The projection of family composition over the life course with family status life tables (Bongaarts)

• Amemiya, T. (1985) Advanced econometrics. Harvard University Press, Cambridge. Chapter 11: Markov chain and duration models.
• Andersen, P.K. (1985) Statistical models for longitudinal labor market data based on counting processes. In: J.J. Heckman and B. Singer eds. Longitudinal analysis of labor market data. Cambridge University Press, Cambridge, pp. 294-307.
• Andersen, P.K. (1988) Multistate models in survival analysis: a study of nephropathy and mortality in diabetes. Statistics in Medicine, 7:661-670. Reprinted in D.J. Bogue, E.E. Arriaga and D.L. Anderton eds. Readings in population research methodology. Social Development Center, Chicago, and UNFPA, New York, pp. 22.39-22.48
• Attema, J.W. (1997) Multistate insurance. Master Thesis, Faculty of Economics, University of Groningen.
• Barendregt, J.J., L. Bonneux, and P.J. van der Maas (1994) Health expectancy, an indicator for change? Journal of Epidemiology and Community Health, 48:482-487.
• Barendregt,J.J., J. Bonneux and P.J. Van-der-Maas (1998) Health Expectancy: From a Population Health Indicator to a Tool for Policy Making, Journal of Aging and Health, 10(2):242-258
• Barendregt,  J.J., G.J. van Oortmarssen, B.A. van Hout, J.M. van den Bosch and L. Bonneux (1998) Coping with multiple morbidity in a life table. Mathematical Population Studies, 7(1):29-49 Also published in : J.J. Barendregt and L. Bonneux (1998) Degenerative disease in an aging population. Models and conjectures. PhD dissertation, Erasmus University, Rotterdam, Chapter 12.
• Belanger, A. (1990) Multistate life table with duration dependence: an application to Hungarian female marital history. European Journal of Population, 5(4):347-372
• Belanger, A. and D. Larrivee (1992) New approach to constructing Canadian working life tables, 1986-1987. Statistical Journal, 9(1):27-49
• Biritwum, R.B. and S.I.K. Odooms (1995) Application of Markov process modelling of health status switching behaviour of infants. International Journal of Epidemiology, 24:177-182.
• Blossfeld, H.P. and G. Rohwer (1995) Techniques of event history modeling. New approaches to causal analysis. Lawrence Erlbaum, Mawah, New Jersey.
• Bongaarts, J., T. Burch and K. Wachter eds. Family demography: methods and their applications. Clarendon Press, Oxford.
• Bongaarts, J. (1987) The projection of family composition over the life course with family status variables. In: J. Bongaarts, T. Burch and K. Wachter eds. Family demography: methods and their applications. Clarendon Press, Oxford, pp. 189-212 (Paperback in 1990). Reprinted in D.J. Bogue, E.E. Arriaga and D.L. Anderton eds. Readings in population research methodology. Social Development Center, Chicago, and UNFPA, New York, pp. 22.97-22.108.
• Broersma, L., F.A.G. den Butter and U. Kock (2000) A national accounting system for worker flows. Economics Letters, 67:331-336
• Calhoun, C.A. and T.J. Espenshade (1985) The opportunity cost of rearing American children. Urban Institute, Washington (impact of childbearing on hours worked is assessed using tables of working life)
• Caswell, H. (2001) Matrix population models: construction, analysis and interpretation. 2nd ediction. Sinauer Associate, Sunderland, Mass.
• Chiang, C.L. (1984) The life table and its applications. R.E. Krieger Publishing Company, Malabar, Florida
• Congdon, P. (1992) Multiregional demographic projections in practice: a metropolitan example. Regional Studies, 26(2):177-191.
• Courgeau, D. and J. Najim (1995) Analyse de biographies fragmentaires [Analysis of incomplete event histories]. Population, 50(1):149-68 (in French)
• Crimmins,E.M., M.D. Hayward, and Y. Saito (1994) Changing mortality and morbidity rates and the health status and life expectancy of the older population. Demography, 31(1), pp. 159-75.
• Darsky, L. and S. Scherbov (1995) Marital status behaviour of women in the former Soviet Republics. European Journal of Population, 11(1):31-62
• Den Butter, F. and C. Gorter (1999) Modelling labour market dynamics with on-the-job search. Economic Modelling, 16(4):545-567
• Devine, T.J. and N.M. Kiefer (1991) Empirical labor economics: the search approach. Oxford University Press, New York. (Multistate search models)
• Dow, M.M. (1985) Nonparametric inference procedures for multistate life table analysis. Journal of Mathematical Sociology, 11:245-263
• Ehrenberg, R.G. and R.S. Smith (1982) Modern labor economics. Theory and public policy. Scott, Foresman and Co., Glenview, Ill. Section "The demographic structure of unemployment rates" (pp. 440-445).
• Espenshade, T.J. (1987) Marital careers of American women: a cohort life table analysis. In: J. Bongaarts, T. Burch and K. Wachter eds. Family demography: methods and their applications. Clarendon Press, Oxford, pp. 150-167.
• Espenshade, T.J. and R.E. Braun (1982) Life course analysis and multistate demography: an application to marriage, divorce, and remarriage. Journal of Marriage and the Family, ???, pp. 1025-1036. Reprinted in D.J. Bogue, E.E. Arriaga and D.L. Anderton eds. Readings in population research methodology. Social Development Center, Chicago, and UNFPA, New York, pp. 13.19-13.30
• Flinn, C. and J. Heckman (1982a) New methods for analyzing structural models of labor force dynamics. Journal of Econometrics, 18:115-168.
• Flinn, C. and J. Heckman (1982b) Models for the analysis of labor market dynamics. In: R. Bassmann and G. Rhodes eds. Advances in econometrics. Vol. 3, JAI Press, Greenwich, Conn.
• Foster, W.F. and R.G. Gregory (1983) A flow analysis of the labour market in Australia. In: R. Blandy and O. Covick eds. Understanding labour markets in Australia. George Allen & Unwin, Sidney, pp. 111-136.
• Gentleman, R.C., K.F. Lawless, J.C. Lindsey and P. Yan (1994) Multi-state Markov models for analysing incomplete disease history data with illustrations for HIV disease. Statistics in Medicine, 13(8):805-821.
• Gill, R.D. (1992) Multistate life tables and regression models. Mathematical Population Studies, 3(4):259-276
• Gill, R.D. and N. Keilman (1990) On the estimation of multidimensional demographic models with population registration data. Mathematical Population Studies, 2(?):119-143.
• Ginsberg, R.B. (1971) Semi-Markov processes and mobility. Journal of Mathematical Sociology, 1:233-262.
• Haberman, S. (1984) Decrement tables and the measurement of morbidity: II. Journal of the Institute of Actuaries, 111(1):73-86
• Hachen, D.S. (1988) The competing risk model. Sociological Methods and Research, 17(1):21-54. Reprinted in in D.J. Bogue, E.E. Arriaga and D.L. Anderton eds. Readings in population research methodology. Social Development Center, Chicago, and UNFPA, New York, pp. 21.85-21.101.
• Halli, S.S. and K.V. Rao (1992) Advanced techniques of population analysis. Plenum Press, New York. Chapter 9. Multiregional demographic analysis.
• Hannan, M.T. (1984) Multistate demography and event history analysis. In: A. Diekmann and P. Mitter eds. Stochastic modeling of social processes. Academic Press, Orlando, pp. 39-88.
• Hansen, P.E. (1989) Leslie matrix models. Mathematical Population Studies, 2(1):37-67
• Hansen, B.E., J. Thorogood, J. Hermans, R.J. Ploeg, J.H.van Bockel and J. van Houwelingen (1994) Multistate Modelling of Liver Transplantation Data, Statistics in medicine, 13(23-24), p. 2517-2530
• Hartmann, A., G. Schulgen, M. Olschewski and T. Herzog (1997) Modeling Psychotherapy Outcome as Event in Time: An Application of Multistate Analysis, Journal of consulting and clinical psychology, 65(2), p. 262-268
• Hayward, M.D., E.M. Crimmins and Y. Saito (1998) Cause of death and active life expectancy in the older population of the United States. Journal of Aging and Health, 10(2):192-213
• Hayward, M.D. and D.T. Lichter (1998) A life cycle model of labor force inequality: extending Clogg’s life table approach. Sociological Methods and Research, 26(4):487-510
• Hayward, M.D., C. Hsinmu and E.M. Crimmins (??) Evaluating group differences in healthy life expectancy: the estimation of confidence intervals for multistate life table expectancies. Manuscript.
• Heligman, L. and J.H. Pollard (1980) The age pattern of mortality. Journal of Institute of Actuaries, 107:49-75. Reprinted in: D.J. Bogue, E.E. Arriaga and D.L. Anderton eds. Readings in population research methodology. Social Development Center, Chicago, and UNFPA, New York, pp. 7.98-7.104
• Hesselager, O. and R. Norberg (1996) On probability distributions of present values in life insurance. Insurance: Mathematics and Economics, 18(1):35-42 (multistate life insurance policy under Markov assumption)
• Hoem, J.H. (1977) A Markov chain model of working life tables. Scandinavian Actuarial Journal, 1977, p. 1-20.
• Hoem, J.M. and U.F. Jensen (1982) Multistate life table methodology: a probabilist critique. In: K.C. Land and A. Rogers eds. Multidimensional mathematical demography. Academic Press, New York, pp. 155-264.
• Hofferth, S.L. (1987) Recent trends in the living arrangements of children: a cohort life table analysis. J. Bongaarts, T. Burch and K. Wachter eds. Family demography: methods and their applications. Clarendon Press, Oxford, pp. 168-188.
• Hougaard, P. (1999) Multi-state models: a review. Lifetime Data Analysis, 5(3):239-264.
• Hougaard, P. (2000) Analysis of multivariate survival data. Springer Verlag, New York.
• Islam, M.A. (1994) Multistate survival models for transitions and reverse transitions: an application to contraceptive use data, Journal of the Royal Statistical Society, 157(3):441-456
• Kay, R. (1986) A Markov model for analysing cancer markers and disease states in survival studies. Biometrics, 42:855-865.
• Keilman, N. (1988) Dynamic household models. In: N. Keilman, A. Kuijsten and A. Vossen eds. Modeling household formation and dissolution. Clarendon Press, Oxford, pp. 123-138
• Keyfitz, N. (1985) Applied mathematical demography. Second Edition. Springer Verlag, New York. Chapter 12. The multistate model. (pp.350-367).
• Keyfitz, N. (1991) A Markov chain for calculating the durability of marriage. Mathematical Population Studies, 1(1):101-121
• Keyfitz, N. and A. Rogers (1982) Simplified multiple contingency calculations. Journal of Risk and Insurance, 49:59-72.
• Khatun, M. and F.J. Willekens (2001) The life history calendar. Technical aspects of data analysis, illustrated using the Bangladesh Demographic and Health Survey (BDHS) 1996-97. Working Paper, Population Research Centre, University of Groningen.
• Krishnamoorthy, S. (1982) Marital status life table for Australian women, 1971. Genus, 38(1-2):97-117.
• Kuo, H.S., H.J. Chang, P. Chou, L. Teng and R.H.H. Chen (1999) A Markov chain model to assess the efficacy of screening for non-insulin dependent diabetes mellitus (NIDDM), International Journal of Epidemiology, 28:233-240.
• Land, K.C. and G.C. Hough, Jr. (1989) New methods for tables of school life, with applications to US data from recent school years. Journal of the American Statistical Association, 84:63-75
• Land, K.C. , G.C. Hough and M.M. McMillen (1986) Voting status life tables for the United States, 1968-1980. Demography, 23:381-402.
• Land, K.C. and A. Rogers eds. (1982) Multidimensional mathematical demography. Academic Press, New York.
• Land, K.C., J.M. Guralnik and D.G. Blazer (1994) Estimating increment-decrement life tables with multiple covariates from panel data: the case of active life expectancy. Demography, 31(2):297-319
• Lawless, J.F. and D.Y.T. Fong (1999) State duration models in clinical and observational studies. Statistics in Medicine, 18:2365-2376
• Ledent, J. (1980) Multistate life tables: movement versus transition perspectives. Environment and Planning A, 12(5):533-562.
• Ledent, J. (1982) Transition probability estimation in increment-decrement life tables using mobility data from a census or survey. In: K.C. Land and A. Rogers eds. Multidimensional mathematical demography. Academic Press, New York, pp. 347-384.
• Ledent, J. and Ph. Rees (1986) Life tables. In: A. Rogers and F. Willekens eds. Migration and settlement: a multiregional comparative study. Dordrecht, The Netherlands: Reidel Press, pp. 386-418.
• Leeves, G.D. (1997) Labour market gross flows and transition rates 1980-1992. Economic and Labour Relations Review, 8(1):110-127
• Liang, J., X. Liu, E. Tu, and N. Whitelaw (1996) Probabilities and lifetime durations of short-stay hospital and nursing home use in the United States, 1985. Medical Care, 34(10): 1018-1036
• Lin, G. (1999) Assessing structural change in U.S. migration patterns: a log-rate modeling approach. Mathematical Population Studies, 7(3):217-237
• Lindeboom, M. and M. Kerkhofs (2000) Multistate models for clustered duration data – An appliction to workplace effects on individual absenteeism. The Review of Economics and Statistics, 82(4):668-684 (available through EBSCO).
• Liu, X., J. Liang, E.J.C. Tu and N. Whitelaw (1997) Modeling Multidimensional Transitions in Health Care, Sociological Methods and Research, 25(3):284-317.
• Macura, M. (1989) Methods to project enrollment by school level and population by level of education. Proceedings of the International Population Conference, New Delhi, 1989, Vol. 3. IUSSP, Liege, pp. 23-39. Reprinted in D.J. Bogue, E.E. Arriaga and D.L. Anderton eds. Readings in population research methodology. Social Development Center, Chicago, and UNFPA, New York, pp. 18.105-18.112.
• Mamun, A.A. (2000) Multistate models in public health. A review and an application to the Framingham Hearth Study. Masters Thesis Demography, Population Research Centre, Faculty of Spatial Sciences, University of Groningen
• Manton, K.G. and E. Stallard (1988) Chronic disease modeling. Griffins, London.
• Marshall, A.W. and H. Goldhammer (1955) An application of Markov processes to the study of the epidemiology of mental disease. Journal of the American Statistical Association, 50:99-129.
• Mathers, C.D. and J.M. Robine (1997) How good is Sullivan's method for monitoring changes in population health expectancies? Journal of Epidemiolofy and Community Health, 51(1): 80-86.
• Medina, S. (2000) Human resources and population in Mexico at the dawn of the twenty-first century. Thela Thesis Publishers, Amsterdam (PhD dissertation, University of Groningen)
• Mills, M. (1999) Construction of input data for log-linear models of event histories. Working Paper 99-3, Population Research Centre, University of Groningen, Groningen, The Netherlands.
• Mills, M. (2000) The transformation of partnerships. Canada, the Netherlands and the Russian Federation in the age of modernity. Thela Thesis, Amsterdam (PhD dissertation, University of Groningen)
• Mode, C.J. (1982) Increment-decrement life tables and semi-Markovian processes from a sample path perspective. In: K.C. Land and A. Rogers eds. Multidimensional mathematical demography. Academic Press, New York, pp. 535-566.
• Murthi, B.P.S. and K. Srinivasan (1999) Consumer’s extent of evaluation in brand choice. Journal of Business, 72(2):229-256 (multistate choice model)
• Nair, P.S. (1981) India’s population. A multiregional demographic analysis. Working Paper, Voorburg: NIDI
• Namboodiri, K. (1984) Matrix algebra: and introduction. Sage University Paper, Sage Publications, Beverly Hills (several copies available in Groningen; not placed on reservation)
• Namboodiri, K. (1990) Demographic analysis. A stochastic approach. Academic Press, San Diego.
• Namboodiri, K. and C.M. Suchindran (1987) Life table techniques and their applications. Academic Press, Orlando.
• Newman, S.C. (1988) A Markov process interpretation of Sullivan’s index of morbidity and mortality. Statistics in Medicine, 7:787 - 794
• Nour, E.S. and C.M. Suchindran (1983) A general formulation of the life table. Mathematical Biosciences, 63:241-252
• Nour, E.S. and C.M. Suchindran (1984) The construction of multi-state life tables: comments on the article by Willekens et al., Population Studies, 38:325-328.
• Nour, E.S. and C.M. Suchindran (1984) Multi-state mortality by cause of death: a life table analysis. Journal of the Royal Statistical Society, Series A, 147(4):582-597
• Nour, E.S. and C.M. Suchindran (1985) Multistate life tables: theory and application. In: P.K. Sen ed. Biostatistics. Statistics in biomedical, public health and environmental sciences. North Holland, Amsterdam, pp. 143-162.
• Nusselder, W. (1998) Compression or expansion of morbidity. A life-table approach. PhD Dissertation. Erasmus University, Rotterdam. Chapter 5: A multistate life table of health expectancy using pooled data from different longitudinal studies (pp. 85-128).
• Pitacco, E. (1995) Actuarial models for pricing disability benefits: towards a unifying approach. Insurance: Mathematics and Economics, 16(1):39-62 (Multistate models in health insurance)
• Preston, S.H. (1982) Relations between individual life cycles and population characteristics. American Sociological Review, 47:253-264.
• Preston, S. P. Heuveline and M. Guillot (2001) Demogaphy: meaurement and modeling population processes. Blackwell Publ., Oxford. Chapter on multistate models.
• Rajulton, F. (1987) Event-history analysis in multiregional demography: review of problems and implications for research, Population Studies and Training Center Working Paper Series 87-02, Brown University, Providence, Rhode Island (27 p.)
• Rajulton, F. (1992a) Life history analysis in demography: implications for teaching and research, Canadian Journal in Population, 19(1):1-16
• Rajulton, F. (1992b) Life history analysis: guidelines for using the program LIFEHIST (PC version). Discussion Paper no. 92-5, Population Centre, University of Western Ontario, London, Ontario, Canada
• Rajulton, F. and H.Y. Lee (1988) A semi-Markov approach to using event-history data in multiregional demography. Mathematical Population Studies, 1:289-315.
• Ramachandran, P. (1988) Subnational population projections for India: the use of incomplete data. PhD Dissertation, Sri Venkateswara University, Tirupati, Andhra Pradesh, India
• Ravanera, Z.R. and F. Rajulton (1996) Stability and Crisis in the Family Life Course-Findings from the 1990 General Social Survey, Canada, Canadian Studies in Population, 23(2):165-184.
• Ravanera, Z.R., F. Rajulton and R.K. Burch (1994) Tracing the Life Courses of Canadians, Canadian Studies in Population, 21(1):21-34.
• Rees, Ph. (1986) Choices in the construction of regional population projections. In: R. Woods and Ph. Rees eds. Population structures and models. Allen and Unwin, London, pp. 126-159.
• Rees, Ph. And F.J. Willekens (1986) Data and Accounts. In: A. Rogers and F. Willekens eds. Migration and settlement: a multiregional comparative study. Dordrecht, The Netherlands: Reidel Press, pp. 19-58.
• Rees, Ph. And F.J. Willekens (1989) Population projection: Dutch and English multiregional methods. In: J. Stillwell and H.J. Scholten eds. Contemporary research in population Geography. A comparison of the United Kingdom and the Netherlands. Kluwer Academic Publishers, Dordrecht, pp. 19-37.
• Richards, T., M.J. White and A.O. Tsui (1987) Changing living arrangements: a hazard model of transitions among household types. Demography, 24(1):77-97. Reprinted in D.J. Bogue, E.E. Arriaga and D.L. Anderton eds. Readings in population research methodology. Social Development Center, Chicago, and UNFPA, New York, pp. 15.77-15.87
• Ridder, G. (1987) Life cycle patterns in labor market experience. A statistical analysis of labor market histories of adult men. PhD dissertation. University of Amsterdam.
• Rogers, A. (1971) Matrix methods in urban and regional analysis. Holden-Day, San Francisco. No copy in Groningen. Copies at several other university libraries in the Netherlands.
• Rogers, A. (1973) The multiregional life table. Journal of Mathematical Sociology, 3:127-137.
• Rogers, A. (1975) Introduction to multiregional mathematical demography. Wiley, New York.
• Rogers, A. ed. (1980) Essays in multistate mathematical demography. Special issue of Environment and Planning A, 12(5):485-622
• Rogers, A. (1985) Regional population projection models. Sage Publications, Beverly Hills.
• Rogers, A. (1986) Parameterized multistate population dynamics and projections. Journal of the American Statistical Association, 81(?):48-61. Reprinted in D.J. Bogue, E.E. Arriaga and D.L. Anderton eds. Readings in population research methodology. Social Development Center, Chicago, and UNFPA, New York, pp. 22.83-22.9
• Rogers, A. (1990) The multistate stable population model with immigration. Mathematical Population Studies, 2(4):313-324.
• Rogers, A. (1992) Heterogeneity and selection in multistate population analysis. Demography, 29(?):31-38.
• Rogers, A. (1995) Multiregional demography. Principles, methods and extensions. Wiley, Chichester.
• Rogers, A. and A. Belanger (1990) The importance of place of birth in migration and population redistribution analysis. Environment and Planning A, 22(?):193-210.
• Rogers, A., R.G. Rogers and L.C. Branch (1989) A multistate analysis of active life expectancy. Public Health Reports, 104(3):222-226.
• Rogers, R.G., A. Rogers and A. Belanger (1989) Active life among the elderly in the United States: multistate life-table estimates and population projections. Milbank Quarterly, 67(3-4):370-411
• Rogers, A. and L. Castro (1982) Model schedules in multistate demographic analysis: the case of migration. In: K.C. Land and A. Rogers eds. Multidimensional mathematical demography. Academic Press, New York, pp. 113-154.
• Rogers, A. and L. Castro (1986) Migration. In: A. Rogers and F. Willekens eds. Migration and settlement: a multiregional comparative study. Dordrecht, The Netherlands: Reidel Press, pp. 157-208.
• Rogers, A. and J. Ledent (1976) Increment-decrement life tables: a comment. Demography, 13(2):287-290.
• Rogers, A. and F. Willekens (1978) The spatial reproductive value and the spatial momentum of zero population growth. Environment and Planning A, 10:503-518.
• Rogers, A. and F.J. Willekens (1986) A short course on multiregional. In: A. Rogers and F. Willekens eds. Migration and settlement: a multiregional comparative study. Dordrecht, The Netherlands: Reidel Press, pp. 355-384.
• Rogers, A., F. Willekens and J. Raymer (2001) The indirect estimation of migration flows. Paper presented at the annual meeting of the Population Association of America, Washington D.C., April 2001. Submitted to Demography
• Rogers, A. and J. Woodward (1991) Assessing state population projections with transparent multiregional demographic models. Population Research and Policy Review, 10:1-26.
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• Schettkat, R. ed. (1996) The flow analysis of labour markets. Routledge, London.
• Schmertmann, C.P. (1999a) Fertility estimation from open birth-interval data. Demography, 36(4):505-519
• Schmertmann, C.P. (1999b) Estimating multistate transtion hazards from last-move data. Journal of the American Statistical Association, 94:53-63
• Schmertmann, C.P., A.A. Amankwaa and R.D. Long (1998) Three Strikes and You're Out: Demographic Analysis of Mandatory Prison Sentencing, Demography, 35(4):445-463 (a multistate life-table model of population flows to and from prisons, incorporating age-specific transition rates estimated from administrative data from Florida)
• Schoen, R. (1975) Constructing increment-decrement life tables. Demography, 12(2):313-324.
• Schoen, R. (1988a) Modeling multigroup populations. Plenum Press, New York.
• Schoen, R. (1988b) Practical uses of multistate population models. Annual Review of Sociology, 14:341-361.
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• Schaubel, D.E., H.I. Morrison, M. Desmeules, D. Parsons and S.S.A. Fenton (1998) End-stage renal disease projections for Canada to 2005 using Poisson and Markov models. International Journal of Epidemiology, 27:274-281.
• Shavelle, R. and D. Strauss (1999) A long period multistate life table using micro data. Mathematical Population Studies, 7(2):161-177
• Shen, J. (1993) Analysis of urban-rural population dynamics in China: a multiregional analysis. Environment and Planning A, 25(2):245-253
• Sinha, R.K. (1994) Marriage and marital dissolution in India: a multistate life table analysis. IIPS Research Report Series No. 15, International Institute for Population Sciences [IIPS], Mumbai.
• Stoto, M.A. (1988) Estimating age-specific transition rates for population sub-groups from successive surveys: changes in adult rates of cigarette smoking. Population Studies, 42(2):227-239
• Strauss D. and R.Shavelle (1998) An extended Kaplan-Meier estimator and its applications, Statistics in Medicine, 17(9): 971-82
• Sweeney, S.H. (1999a) Model-based incomplete data analysis with an application to occupational mobility and migration accounts. Mathematical Population Studies, 7(3):279-305
• Sweeney, S.H. (1999b) Analysis of incomplete data in a log-linear/GLM framework: theory, examples and computer programs. SOM Research Report, SOM, University of Groningen
• Tan, K.C. and R.J. Bennett (1984) Optimal control of spatial systems. Allen and Unwin, London.
• Tas, R.F.J. (1992) Period life tables for the Netherlands by age, sex and province, 1986-1990. Maandstatistiek Bevolking, 92/4, pp. 19-33 (in Dutch) (multiregional life tables)
• Taylor, H.M. and S. Karlin (1994) An introduction to stochastic modeling. Revised Edition. Academic Press, Boston.
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• Van Imhoff, E. (1990) The exponential multidimensional demographic projection model. Mathematical Population Studies, 2(3):171-182.
• Van Imhoff, E. (1992)  A general characterization of consistency algorithms in multidimensional demographic projection models. Population Studies.  46/1, pp. 159-169.
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