1. Southern Northwestern Central VolgaUral Siberian Far Eastern Simple projections only account for age To incorporate inter-regional migration, we must also account for region of residence and movement between the regions Introduction This poster explores the long-term population impacts of assumptions about the extent to which migrants adjust their demographic behaviors after moving to different regions of a country In Russia, this may be an important component of population change because there is considerable regional variation in fertility and mortality Methods Regional fertility and mortality levels, as well as inter-regional migration rates were collected or projected Using the cohort component method of population projection within each region, migrants were allowed to move between regions at a constant rate Four fertility scenarios were tested (from the United Nations’ assumptions about fertility change in Russia) Four migrant adjustment scenarios were tested –1) Adjustment: Migrants change their fertility and mortality propensities to the fertility and mortality patterns prevalent at destination immediately after migrating –2) Assimilation: Migrants retain the fertility and mortality patterns prevalent in origin areas, but their children exhibit the demographic patterns prevalent in destinations –3) Disruption: Migrants do not bear children for five years after migrating but retain the mortality patterns prevalent at origin After five years migrants return to the fertility patterns prevalent at origin –4) Selection: Migrants retain the fertility and mortality patterns prevalent at origin and experience no change after migrating Adjusting for Adjustment: Impacts of the Assimilation, Adaptation, Disruption and Selection Hypotheses of Migrant Demographic Adjustment in a Context of Regional Variation, the Case of Russia Acknowledgements and Contact Information Acknowledgements: This research benefited greatly from the insight of Barbara Entwisle, John Sandberg, and Tom Swasey Participation in the conference and portions of the work were funded by a training grant from the National Institute of Child Health and Human Development (NIH/NRSA T32 HD07168), the Carolina Population Center and the department of Sociology at the University of North Carolina at Chapel Hill Contact: Ashton Verdery The University of North Carolina at Chapel Hill Carolina Population Center CB# 8120, University Square 123 West Franklin Street Chapel Hill, NC 27516-2524 verdery@email.unc.edu Study Area: The Federal Districts of Russia Conclusions Internal migration occurs in all societies, yet is rarely, if ever, incorporated into population projections Doing so necessitates exploring how migrants transfer between regional fertility and mortality risks To do this, I have relied on research about migrant demographic adjustment to derive four potential hypotheses I asked whether these simple assumptions about demographic change might alter long-term population growth My adjustment models do not have as large an impact as fertility assumptions, though they have interesting interactions with fertility scenarios This poster has identified that while migrant demographic adjustment is interesting in its own right, it also may impact long-term population processes such as growth rates Future work should build on this finding, identify truer trajectories of change for migrants in different contexts, and incorporate them into population projections A more complete picture of migrant fertility and mortality adjustment may aid in making more accurate population projections Demographic research has pointed out four conceptual models of migrant adjustment: Primary Question: Does assuming such changes make a difference for long-term population growth? Why Situate These Questions in Russia? Figure 3 Net Migration and Natural Increase in Rural and Urban Areas of the Russian Federation, 1960-2005 Notes: Prior to 1992 data is for the RSFSR of the Soviet Union while after 1992 it is for the Russian Federation A hashed, vertical line denotes dissolution of USSR Sources: Years 1960-1989 from Goskomstat 1999: 20-21; years 1990-2005 from Goskomstat 2006, 21-22 Regional Variation in Natural Increase The Population Future is in Question Notes: Prior to 1992 data is for the RSFSR of the Soviet Union while after 1992 it is for the Russian Federation A hashed, vertical line denotes dissolution of USSR Sources: Net reproduction rate: Goskomstat 2005: 133 Figure 2 Changes in Russia’s Net Reproduction Rates, 1961-2005 Four Fertility Scenarios Figure 4 Trends in assumed total fertility rates, 2002-2047 Source: United Nations Population Division, 2006 Figure 1 Absolute population size for the total population under four fertility scenarios with models of the assimilation assumptions, 2002-2052 Results Old Cohort at Time 1 Time 2 Births to Both Cohorts Time 1 Youngest Cohort Young Cohort at Time 2 Old Cohort at Time 2 Young Cohort at Time 1 Young Death Risk Old Death Risk Simple Projections Birth Risks Time 2 Births to Both Cohorts Time 1 Youngest Cohort Young Cohort at Time 2 Old Cohort at Time 2 Young Death Risk Old Death Risk Time 2 Births to Both Cohorts Time 1 Youngest Cohort Young Cohort at Time 2 Old Cohort at Time 2 Young Death Risk Old Death Risk Old Cohort‘s Migration Risk Birth Risks (Population Set A) *(Risk Set A)(Population Set B) *(Risk Set B) Risk of Transition Between Sets Complex, Multi-State Projections Young Cohort at Time 1 Old Cohort at Time 1 Young Cohort’s Migration Risk Yet, what becomes of migrants and their children? Do they experience the fertility and mortality risks prevalent at origin, destination or something else? Research Questions Young Cohort at Time 1 Scenarios and Assumptions In addition to the migrant adjustment scenarios, the models assume constant mortality and inter-regional migration, zero international migration, and test four potential fertility scenarios 1 st Generation Migrants Destination Origin/ Migrants Destination Origin/ Migrants Destination Origin/ Migrants Destination Origin/ Migrants 2 nd Generation Migrants Mean CEB Stylized Models of Assimilation, Adaptation, Disruption & Selection Hypotheses Used As Assumptions Migrants 1 st Generation Migrants Destination Origin/ Migrants Destination Origin/ Migrants Destination Origin/ Migrants Destination Origin/ Migrants 2 nd Generation Migrants Mean CEB Stylized Models of Assimilation, Adaptation, Disruption & Selection Hypotheses Used As Assumptions Migrants Ashton Verdery ■ Carolina Population Center ■ The University of North Carolina at Chapel Hill 2008 AdaptationAssimilation DisruptionSelection Rural Areas -1500 -1000 -500 0 500 1000 1500 Thousand Persons Natrural Increase Net Migration High Fertility Scenarios 100 110 120 130 140 150 160 170 180 1234567891011 Assimilation Assumptions Total Population (Millions) Assimilation Adaptation Selection Disruption Constant Fertility Scenarios 100 110 120 130 140 150 160 170 180 20022007201220172022202720322037204220472052 Assimilation Assumptions Total Population (Millions) Adaptation Assimilation Disruption Selection Urban Areas -1500 -1000 -500 0 500 1000 1500 Thousand Persons Natural Increase Net Migration 1960 1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 1960 1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 0.00 0.20 0.40 0.60 0.80 1.00 1.20 1.40 1.60 1.80 2.00 196219651968197119741977198019831986198919921995199820012004 Net Reproductive Rate Rural Areas Urban Areas