population analysis: terminology estimate projection forecast plan
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Population Analysis: Terminology
Estimate
Projection
Forecast
Plan
Population Projection Techniques
Trend extrapolation models Linear Exponential Modified Exponential
Ratio share
Holding capacity
Cohort component
Components of Population Change
P = N + M
N=natural increase or decrease (i.e., birth or death)
M = net migration
Trend Extrapolation: Linear Model
y = a + bx
P t+n = Pt + b(n)
P = populationPt = population in base year (current time)P t+n = population n periods of time in the futureb = average change per time periodn = number of time periods
Trend Extrapolation: Exponential Model
P t+n = Pt(1 + r)n
r = rate of change per time period
Trend Extrapolation: Modified Exponential Model
P t+n = K – [(K - Pt) vn]
K = maximum capacityv = average portion of unused capacity remaining after each
time period
Ratio-Share
Holding Capacity
Cohort-Component Model
Shortcomings of trend extrapolation techniques:
Aggregated inputs and outputs
No identification of causes of population change
Cohort-Component Model
Perhaps no single factor is more important for local government planning than the size and composition of a region's population and the way it will change in the future. Even though the total population may remain constant, changes in its composition can fundamentally alter the need for public facilities and services.
-Klosterman (1990), p. 51
Cohort-Component Model
Allows for dissagregated view of population change (projects size AND composition)
Directly considers causes of population change (death, birth, migration)
Cohort-Component Model
Components of Population Change in the Model:
Death (survival rate)
Birth (fertility rate)
Migration (adjust by migration rate)
Survival Rates
Probability that a member of an age-sex cohort will survive into the next age
group (E.g., Probability that a female in the 10-14 age group will survive to
be in the 15-19 age group five years from now.)
n+1P t+1 = nPt * n(S)
Age-Sex Cohort Pop in 2000 Survival Rate 2005
F 10-14 10,000 0.998
F 15-19 ?
Time t
- 10,000 20,000 30,000 40,000 50,000
0 to 4 years
5 to 9 years
10 to 14 years
15 to 19 years
20 to 25 years
25 to 29 years
30 to 34 years
35 to 39 years
40 to 44 years
45 to 49 years
50 to 54 years
55 to 59 years
60 to 64 years
65 to 69 years
70 to 74 years
75 to 79 years
80 to 84 years
85 years and over
Time t+1
0 10,000 20,000 30,000 40,000 50,000
0 to 4 years
5 to 9 years
10 to 14 years
15 to 19 years
20 to 25 years
25 to 29 years
30 to 34 years
35 to 39 years
40 to 44 years
45 to 49 years
50 to 54 years
55 to 59 years
60 to 64 years
65 to 69 years
70 to 74 years
75 to 79 years
80 to 84 years
85 years and over
Memphis MSA:Population by Age and Sex, 2000
-5% -4% -3% -2% -1% 0% 1% 2% 3% 4% 5%
0 to 4 years5 to 9 years
10 to 14 years15 to 19 years20 to 25 years25 to 29 years30 to 34 years35 to 39 years40 to 44 years45 to 49 years50 to 54 years55 to 59 years60 to 64 years65 to 69 years70 to 74 years75 to 79 years80 to 84 years85 years and
Male
Female
Export-Base Theory of Growth
Basic industries: produce goods and services for export bring in “new” money depend on external factors (exogenous demand)
Non-basic industries: produce for local consumption (sell products within
the local market) don’t bring in new money Depend on local business conditions
Economic Base Multiplier (k)
Ratio of total employment to basic employment
k = total employmentbasic employment
k * ∆ basic employment = ∆ total employment
Location Quotients
LQ = ei / e
Ei / E
ei = local employment in industry i
e = total local employmentEi = US employment in industry i
E = total US employment
Interpreting Location Quotients
LQ < 1 All employment is non-basic
LQ = 1 All employment is non-basic(locality is exactly self-sufficient)
LQ > 1 Some employment is basic
Calculating Basic Employment
Basic employment i = ei – e(Ei / E)
Caveats of the LQ Approach
Assumptions:
1. Productivity within a specified industry is uniform across all regions
2. Consumption of goods from a given industry is everywhere equal
3. Each industry produces a single homogenous good
Shift-Share Analysis
Partitions local employment growth into 3 components:
National Growth Component
Industrial Mix Component
Competitive Component
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