Population Analysis: Terminology Estimate Projection Forecast Plan

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