Overview of Connecticut Population Projections from 2015 to 2040 Weixing Zhang, Michael Howser, and Qinglin Hu December, 2016

Background and Objective Input Data Methodology Future Work Reference Outline Background and Objective Input Data Methodology Future Work Reference

Background and Objective Input Data Methodology References What we have done Population Projection of Connecticut State, County, Regional Planning Organizations, and Town from 2010 to 2025, with details of age and sex, based on the standard cohort-component projection. Significance ranking #1. Birth Projection #2. Survival Projection #3. Migration Projection   2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Total Percent Birth 42659 41996 42826 42005 41722 41789 41597 40388 38876 37715 411573 11.52% Death 29817 30062 29527 29133 29264 29156 28563 28752 28394 28597 291265 8.15% Residual of Birth and Death 12842 11934 13299 12872 12458 12633 13034 11636 10482 9118 120308 3.37% Residual Net Migration 4822 48224 1.35% Total Population 3405565 3574097

Background and Objective Input Data Methodology References Difficulty ranking #1. Migration Projection #2. Birth Projection #3. Survival Projection Objective The purpose of this project is to incorporate multi-source datasets for producing population projections for each of the 169 towns in Connecticut from 2015 to 2040 by population projection method from UMass Donahue Institute, with details of age and sex. Migration is the most difficult component to estimate and is the most likely source of uncertainty and error in population projections. Because there is not much we can do about birth projection and survival projection but holding fertility rate and survival rate constant over projection period. Fertility and mortality follow fairly regular age-related patterns. However, the migration behavior of similar age groups is influenced by regional and national differences in socio-economic conditions. Furthermore, the data needed to estimate migration is often restricted or limited; especially for many small areas. Migration includes Domestic Migration (In and Out migration), International Migration (immigration and emigration), College Migration. Whereas fertility and mortality follow fairly regular age-related patterns.

Background and Objective Input Data Methodology References Why use population projection method from UMass Donahue Institute? #1. Similar demographic profile #2. Similar migration behavior Migration is the most difficult component to estimate and is the most likely source of uncertainty and error in population projections. Because there is not much we can do about birth projection and survival projection but holding fertility rate and survival rate constant over projection period. Because fertility and mortality follow fairly regular age-related patterns. However, the migration behavior of similar age groups is influenced by regional and national differences in socio-economic conditions. Furthermore, the data needed to estimate migration is often restricted or limited; especially for many small areas. Migration includes Domestic Migration (In and Out migration), International Migration (immigration and emigration), College Migration. Whereas fertility and mortality follow fairly regular age-related patterns.

Background and Objective Input Data Methodology References Why use population projection method from UMass Donahue Institute? #1. Similar demographic profile #2. Similar migration behavior Migration is the most difficult component to estimate and is the most likely source of uncertainty and error in population projections. Because there is not much we can do about birth projection and survival projection but holding fertility rate and survival rate constant over projection period. Because fertility and mortality follow fairly regular age-related patterns. However, the migration behavior of similar age groups is influenced by regional and national differences in socio-economic conditions. Furthermore, the data needed to estimate migration is often restricted or limited; especially for many small areas. Migration includes Domestic Migration (In and Out migration), International Migration (immigration and emigration), College Migration. Whereas fertility and mortality follow fairly regular age-related patterns.

Background and Objective Input Data Methodology References Why use population projection method from UMass Donahue Institute? #1. Similar demographic profile #2. Similar migration behavior Migration is the most difficult component to estimate and is the most likely source of uncertainty and error in population projections. Because there is not much we can do about birth projection and survival projection but holding fertility rate and survival rate constant over projection period. Because fertility and mortality follow fairly regular age-related patterns. However, the migration behavior of similar age groups is influenced by regional and national differences in socio-economic conditions. Furthermore, the data needed to estimate migration is often restricted or limited; especially for many small areas. Migration includes Domestic Migration (In and Out migration), International Migration (immigration and emigration), College Migration. Whereas fertility and mortality follow fairly regular age-related patterns.

Background and Objective Input Data Methodology References Before | Inputs Base Population: 2010 Census Birth Death Now | Inputs Base Population: 2010 Census Birth Death Migration ACS PUMS ACS 5 years estimate PUMA level Net-migration ratio method is either positive or negative. But in reality, net-migration can change over time. Most analysts use a net migration approach, where a single net migration rate is calculated as the number of net new migrants per cohort (in-migrants minus out-migrants) divided by the baseline cohort population of the study region. Although common, the net migration approach suffers from several conceptual and empirical flaws. A major problem is that denominator of the net migration rate is based purely on the number of residents in the study region. However, none of the existing residents are at risk of migrating into the region – they already live there. While this may seem trivial, it has been shown to lead to erroneous and biased projections especially for fast growing and declining regions. Town level

Background and Objective Input Data Methodology References Controlling town-level population projections to PUMA-level total projections that are based on a gross migration model (Renski, Koshgarian, and Strate, 2013) Town Projection Population: 2015 PUMA Projection Population: 2015 Male Male Male Male ( ) x W1 = 90 + + 90 + + 90 + 90 + ( ) x W2 = 85-89 + 85-89 + 85-89 85-89 ( ) x W3 = 80-84 + 80-84 + 80-84 80-84 We will still use the standard cohort-component projection model from previous work since fertility and mortality follow fairly regular age-related patterns. We will focus on solving the dynamic changing “Migration” in PUMA level for final controlling. Before this process, Population projection at PUMA was controlled by ACS population estimate at State level by adjusting in-migration or out-migration!!!!!!!!!! ( ) x W… = 15-19 + 15-19 + 15-19 15-19 ( ) x W… = 10-14 + 10-14 + 10-14 10-14 ( ) x W… = 5-9 + 5-9 + 5-9 5-9 ( ) x W… = 0-4 + 0-4 + 0-4 0-4 North Haven Wallingford Meriden PUMA: 09-01500

Background and Objective Input Data Methodology References References Renski, H., Koshgarian, L., & Strate, S. (2013). Long-term Population Projections for Massachusetts Regions and Municipalities. UMass Donahue Institute. Connecticut State Data Center. (2012). Methodology for Population Projection of Connecticut State, County, Regional Planning Organizations, and Town from 2010 to 2025. Connecticut State Department of Public Health & Connecticut State Data Center. (2016). Small area population estimates project summary report.

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