Quantitative Planning

Front 1

Purpose of Sampling

Back 1

The process of selecting units (e.g.,
people, organizations) from a population of
interest so that by studying the sample we
may generalize our results back to the
population from which they were chosen.
Example: Predicting presidential elections.

Front 2

types of samples

Back 2

• Simple Random Sampling:A method for
choosing cases from a population by which
every case has an equal chance of being
included.
• Stratified Sampling: A method by which cases
are selected from sublistsof the population.
• Cluster Sampling:A method by which
geographical units are randomly selected and
all cases within each selected unit are tested.

Front 3

census samples

Back 3

• Highly complex sampling method.
• Sampling rates (e.g., 1-in-8, 1-in-6, etc.) of
households vary by geographic unit (e.g.,
counties, cities, etc.), depending on past size
(population, number of households).

Front 4

sampling distribution

Back 4

• A theoretical distribution (we never actually
“see” it or know it).
• But based on the laws of probability, we know
its properties (the shape, central tendency,
and dispersion).
• What do we use it for? TO MOVE FROM A
SAMPLE TO A POPULATION.

Front 5

statistically significant difference

Back 5

if two intervals overlap

Front 6

confidence intervals

Back 6

look at more

Front 7

conventions for writing about numbers - spell out in words

Back 7

• At beginning of sentence:
“Three hours a day will produce …”
• In whole numbers one through nine:
“I wrote six letters today.”
• In fractions:
“one-fourth inch”
“two-thirds of a cup”

Front 8

conventions for writing about numbers - use figures

Back 8

• When the number is ten or more.
“I wrote 12 pages today.”
• When numbers below 10 occur with larger
numbers:
“I have ordered 9 cups of coffee and 15
sandwiches to be delivered in one hour.”
(Note “one hour” is not related to the other
numbers and is not written as a figure.)
• When referring to parts of a book:
“chapter 9” / “page 75” / “Table 1”
• When preceding units of time,
measurement, or money:
“8 years old” / “10 yards” / “$4 million”

Front 9

6 key principles for writing about numbers

Back 9

1) set context - who/what/where/when/why
2)choose vocab to suit audience - avoid jargon
3)report and INTERPRET #s
4)specify direction and magnitude - ie. “Among the elderly, mortality roughly
doubles for each successive five-year age group.”
5)choose a fitting # of decimal places - In general, include the smallest number of
decimal places that suit the scale of your
numbers.
6)summarize patterns

Front 10

presenting data with quantitative graphics

Back 10

look at readings

Front 11

differences between acs and census

Back 11

• 2010 Census will focus on counting the U.S. population (for the purpose of apportioning representatives)
• ACS will now handle the task of the old “long form”
(primarily for the purpose of federal funding and programs)

Hint 11

• Census uses point-in-time estimates that describe the
characteristics of an area on a specific date (e.g., April 1,
2000)
• ACS estimates are period estimates, describing
characteristics collected over a specified period

Front 12

ACS Content

Back 12

4 parts
-social
-economic
-housing
-demographic

Front 13

ACS Social

Back 13

•Education
•Marital Status
•Fertility
•Presence of Grandparents /
Caregivers
•Disability Status

Front 14

ACS Economic

Back 14

•Income
•Employment Status
•Occupation
•Industry
•Commute to Work
•Place of Work

Front 15

ACS Housing

Back 15

•Tenure
•Occupancy and Structure
•Housing Value
•Utilities
•Mortgage/Monthly Rent

Front 16

ACS Demographic

Back 16

•Sex
•Age
•Race
•Hispanic Origin

Front 17

ACS Sample

Back 17

• Sample includes about 3 million addresses each year
• Three modes of data collection
–mail
–phone
–personal visit
• Data are collected continuously throughout the year

Front 18

ACS Target Population

Back 18

• Resident population of the United States and Puerto Rico
– Living in housing units and group quarters
• Current residents at the selected address
– “Two month” rule: residency is based on having lived in a unit for the
previous two months
– Different from decennial census residency rule
– So comparing ACS to censuses will be difficult in places with mobile
populations

Front 19

ACS Time Frames

Back 19

• 1-year (65k+), 3-year (20k+), and 5-year estimates will be released for
geographic areas that meet specific population thresholds

Front 20

ACS Comparing data products

Back 20

• Comparing ACS products across years
For example: Comparing a 1-year estimate from 2007 to another 1-
year estimate in 2006
In a few instances comparisons can be misleading due to differences in
questions or methods
• Comparing ACS to Census 2000
Differences between the 2007 ACS and Census 2000 include residence
rules, universes, and reference periods.

Front 21

ACS similarities with census 2000

Back 21

Similarities with Census 2000
•Similar questions and many of the same
basic statistics
•5-year estimates will be produced for same
broad set of geographic areas including
census tracts and block groups

Front 22

ACS differences with census 2000

Back 22

Sample Sizes
Although the goal of ACS is to produce data comparable
to the Census 2000 long form data, so the ACS estimates
will cover the same small areas as Census 2000, but with
smaller sample sizes
Smaller sample sizes for 5-year ACS estimates result in reductions
in the reliability of estimates

Residence Rules
Census uses the “usual residence rule” (where people
spend most of their time, at their “permanent address”),
ACS uses the “current residence rule” or the “two-month
rule” (residency is based on having lived in a unit for the
previous two months)
Comparing ACS to censuses will be difficult in places with
mobile populations

Hint 22

Reference Periods
Instead of once every 10 years, the ACS will provide a
continuous stream of updated information, with data
releases every year
Census 2000 data described the population and housing
at a “point in time” (as of April 1, 2000), while ACS data
describe “periods of time”as:
1-Year
3-Years
5-Years

Subject Definitions
In a few cases, definitions are slightly different
that could affect comparability of some items

Front 23

ACS Margin of error

Back 23

A measure of the precision of an estimate at a
given level of confidence (90%, 95%, 99%)
The difference between an estimate and its upper
or lower confidence bounds
ACS publishes a Margin of Error based on a 90
percent confidence level

• ACS publishes a “Margin of Error” (MOE),
based on a 90-percent confidence level.

Front 24

ACS interpreting margin of error

Back 24

• The published value of MOE indicates that you
can be 90 percent certain that the estimate
and the population value differ by no more
than the value of the MOE
• Can help you avoid misinterpreting small
differences between estimates as significant

Front 25

margin of error example

Back 25

Of males age 15 and over who live in Baltimore, 52.1
percent have never married.
Estimate: 52.1%
Margin of Error: +/-1.7 percentage points
MOE indicates a 90 percent chance that the
estimate of 52.1 percent and the population value
differ by no more than 1.7 percentage points (i.e.,
within the range of 50.4% to 53.8%).

Front 26

ACS Confidence interval

Back 26

A range that is expected to contain the population
value of the characteristic with a known probability.
Created by:
adding the MOE to the estimate (for the upper bound),
and
subtracting the MOE from the estimate (for the lower
bound).

LBCL = EST –MOECL
UBCL = EST + MOECL
Where,
LBCLis the lower bound (at the desired confidence level);
UBCLis the upper bound (at the desired confidence level);
EST is the ACS estimate;
MOECLis the margin of error (at the desired confidence level)

Front 27

confidence interval example

Back 27

Calculation Example for Baltimore City
LB90 = 52.1% –1.7% = 50.4%
UB90= 52.1% + 1.7% = 53.8%
Confidence Interval: 50.4% to 53.8%
We can be 90 percent confident that the interval
from 50.4% to 53.8% contains the population
value of the percentage of males 15 years and
older in Baltimore who have never married.

Front 28

ACS comparing across geographies

Back 28

Only compare the same time period using the
same type of estimate
–1-year estimates to other 1-year estimates
–3-year estimates to other 3-year estimates
–5-year estimates to other 5-year estimates

Front 29

ACS comparing over time

Back 29

better to use non-overlapping time periods

Front 30

projection

Back 30

A conditional statement about the future, 
based on observations from the present and usually 
the recent past. “If‐then” statement. “If population 
continues to grow at 1.2 percent annually, then the 
total population will reach 115,120 by 2010.” Requires 
merely technique.

Front 31

forecast

Back 31

A projection the analyst is willing to endorse 
as likely. A judgmental statement about what the 
analyst believes to be the most likely future. The 
analyst accepts responsibility for choosing among a set 
of alternative assumptions. Requires cognition, 
theorizing, and craft in addition to technique.

Front 32

projections: types of projection methods

Back 32

• Extrapolation Techniques
• Cohort‐Component Models
•Economic Base Projection Models
• Large-Scale  Urban Modeling (e.g., UrbanSim; California Urban Futures Model)

Front 33

projection: Extrapolation Technique Summarized

Back 33

• The extrapolation technique (aka curve fitting) is a simplistic model that 
uses past gross population trends to project future population levels.
• The defining characteristics of trend extrapolation is that future values 
of any variable are determined solely by its historical values.
• Basic Procedure: 
1) Identify overall past trend and fit proper curve 
2) Project future populations based upon your chosen curve
• Advantages: (1) Low data requirements; (2) Very easy methodology; (3) 
Fast; (4) Low resource requirements (money, skills, etc.); (5) Can be as 
accurate as any other method.
• Disadvantages: (1) Uses only aggregate data (do not separately treat 
underlying subcomponents of change); (2) Atheoretical (do not know why
it ’s happening; no explanatory power).
• Key Assumptions: (1) Future will be like past; and/or (2) Future will differ 
from past in systematic ways.

Front 34

projection: The Curves to Be Fit

Back 34

• Linear Curve: Plots a straight line based on the formula:
Y = a + bX
• Geometric Curve: Plots a curve based upon a rate of compounding 
growth over discrete intervals via the formula:  Y = aebX
• Parabolic (Polynomial) Curve: A curve with “one bend” and a 
constantly changing slope. Formula: Y = a + bX + cX2
• Modified Exponential Curve: An asymptotic growth curve that 
recognizes that a region will reach an upper limit of growth. It takes 
the form:  Y = c + abX

Front 35

projection: The Linear Curve (Y = a + bX)

Back 35

• Fits a straight line to population data. Calculated exactly the same as using linear 
regression (least‐squares criterion). Key assumption: Population changes by constant 
absolute increments per unit of time (slope of the line ‐‐ y/x ‐‐ is constant as x increases). 
• Advantages:
‐‐Simplest curve
‐‐Most widely used
‐‐Useful for slow or non‐growth areas
• Disadvantages:
‐‐Rarely appropriate for demographic data
• Example:
Y = 55,000 + 6,000(X)
In plain language, this equation tells us that for each year that passes, we can project an 
additional 6,000 people will be added to the population. So, in 10 years we would project 
60,000 more people using this equation (6,000 * 10).
• Evaluation: Generally used as a starting point for curve fitting.

Front 36

projection: The Geometric Curve (Y = aebX)

Back 36

• In this curve, growth is assumed to be compounded at set intervals using a constant 
growth rate. To transform this equation into a linear equation, we use logarithms. Key 
assumption: Population changes at a constant rate per unit of time. Like compounding 
interest in a bank account. (The slope of the line increases as x increases).
• Advantages:
‐‐Assumes a constant rate of growth
‐‐Still simple to use
• Disadvantage:
‐‐Does not take into account a growth limit
• Example:
Y = 55,000 * (1.00 + 0.06)X
In plain language, this equation tells us that we have a 6% growth rate. After one year we 
project a population of 58,300. After 10 years we would project a population of 98,497.
• Evaluation: Pretty good for short term fast‐growing areas. However, over the long‐run, 
this curve usually generates unrealistically high numbers.‐‐Still si

Front 37

projection: The Parabolic Curve (Y = a + bX + cX2)

Back 37

• Has a constantly changing slope and one bend. Very similar to the Linear Curve except 
for the additional parameter (c). Growing very quickly when c > 0, declining quickly when c 
< 0. Key assumption: Population rate of change of rate of change is constant. In other 
words, a constantly changing slope. {In calculus, the second derivative is constant}.
• Advantage:
‐‐Models fast growing areas
• Disadvantages:
‐‐Poor for long range projections
‐‐No Growth Limit
‐‐More complex
• Example:
Y = 20,000 + 8.78(X) + 0.581(X2)
When X=0, Y =20,000. When X = 6, Y = 20,074
• Evaluation: Same as the Geometric Curve: good for fast growing areas, but poor over the 
long run.

Front 38

projection: Modified Exponential Curve (Y = c + abX )

Back 38

• An asymptotic curve: Takes into account an upper or lower limit when computing 
projected values. The asymptote can be derived from local analysis or supplied by the 
model itself. Key assumption: Population changes rapidly in early years, then in later years 
tapers off as it approaches a limit (either upper or lower).
• Advantage:
‐‐Growth limit is introduced
‐‐“Best fitting” growth limit
• Disadvantage:
‐‐Much more complex calculations
‐‐ “Growth limit ” may be misleading (high and low)
• Example:
Yc = 114 ‐ 64(0.75)X
The growth limit is 114. The curve takes into account the number of time periods and as X 
gets larger the closer you get to the Growth limit. When X = 0, Y = 50; when X = 2, Y = 78, 
etc. 
• Evaluation: This curve largely depends upon the growth limit. If the limit is reasonable, 
then the curve can be a good one. 

Front 39

projection: Basic principle when using the extrapolation technique 
effectively:

Back 39

The choice of the Base Period can have a significant  impact 
upon the projection generated.

Front 40

economic base: labor force positions

Back 40

employment - employed, unemployed, not in labor force
other considerations - education, unionization rates

Front 41

economic base - occupations

Back 41

• Classification based on the Standard Occupational
Classification (SOC) Manual which includes a
hierarchical structure showing 23 major occupational
groups divided into 96 minor groups, 449 broad
groups, and 821 detailed occupations.
• Major groups reported in census: Managerial and
professional specialty; Technical, sales, and
administrative support; service occupations; farming,
forestry, and fishing; precision production, craft, and
repair; and operators, fabricators, and laborers.

Front 42

economic base - class of worker

Back 42

Categorizes people according to the type of
ownership of the employing organization.

Front 43

econ base: industry

Back 43

• Two classification systems used in the U.S.
1. SIC (Standard Industrial Classification): 1920s-1997
2. NAICS (North American Industry Classification
System): 1997 –current
• Change from SIC to NAICS due to several factors,
largely to more accurately reflect an emerging
postmodern economy that emphasizes services
and technology over manufacturing.
• Not directly comparable: Makes for difficult
longitudinal analysis spanning 1997.

Front 44

econ base: Standard Industrial Classification(SIC) Codes

Back 44

• Classify industries according to similarities in products, services,
and production and delivery systems.
• Codes organize industries in an increasing level of detail ranging
from general economic sectors (i.e. manufacturing, services) to
specific industry segments (i.e. commercial sports, laundry
businesses)
• Basic10categories(orDivisions)areidentifiedbyaletter
• DivisionsaredividedintoMajorGroups(2digitSICcodes)
• MajorGroupsaredividedintospecificIndustryGroups(3digit
SICcodes)
• Industry groups are then subdivided into Industriesthemselves
(4 digit SIC codes)

Front 45

economic base: labor force positions

Back 45

employment - employed, unemployed, not in labor force
other considerations - education, unionization rates

Front 46

economic base - occupations

Back 46

• Classification based on the Standard Occupational
Classification (SOC) Manual which includes a
hierarchical structure showing 23 major occupational
groups divided into 96 minor groups, 449 broad
groups, and 821 detailed occupations.
• Major groups reported in census: Managerial and
professional specialty; Technical, sales, and
administrative support; service occupations; farming,
forestry, and fishing; precision production, craft, and
repair; and operators, fabricators, and laborers.

Front 47

economic base - class of worker

Back 47

Categorizes people according to the type of
ownership of the employing organization.

Front 48

econ base: industry

Back 48

• Two classification systems used in the U.S.
1. SIC (Standard Industrial Classification): 1920s-1997
2. NAICS (North American Industry Classification
System): 1997 –current
• Change from SIC to NAICS due to several factors,
largely to more accurately reflect an emerging
postmodern economy that emphasizes services
and technology over manufacturing.
• Not directly comparable: Makes for difficult
longitudinal analysis spanning 1997.

Front 49

econ base: Standard Industrial Classification(SIC) Codes

Back 49

• Classify industries according to similarities in products, services,
and production and delivery systems.
• Codes organize industries in an increasing level of detail ranging
from general economic sectors (i.e. manufacturing, services) to
specific industry segments (i.e. commercial sports, laundry
businesses)
• Basic10categories(orDivisions)areidentifiedbyaletter
• DivisionsaredividedintoMajorGroups(2digitSICcodes)
• MajorGroupsaredividedintospecificIndustryGroups(3digit
SICcodes)
• Industry groups are then subdivided into Industriesthemselves
(4 digit SIC codes)

Front 50

econ base: NAICS

Back 50

• Beginning with the 1997 Economic Census, NAICSreplaced
SIC.
• Expands upon the major categories of the economy; now 20
“Sectors” instead of 10 Divisions
• Includes new economic sectors: Information sector, Health
Care and Social Assistance sector, and Professional, Scientific,
and Technical sector (better reflecting the New Economy)
• Expands several Divisions into multiple Sectors: For example,
Transportation/Public Utilities was subdivided into 1) Utilities,
2) Transportation and Warehousing
• Deals better with corporations with diverse activities.
• Developed jointly by the U.S., Mexico, and Canada to allow for
a high level of comparability in data among the North
American countries.

Front 51

Comparing Population and Economic Analysis

Back 51

change
-population - part of biological process - not a lot of rapid change
-econ - tied to unpredictable entities, much shorter periods of observable trends

predictability
-pop - relatively predictable
-econ- very unpredictable - lots of variation (seasonal variation, cyclical variation, secular trend)

unit of analysis
-pop- people, households, geographic aggregation

econ - employment, firms, output, wages, profits, sectors

Front 52

econ base: big 3 sources for data

Back 52

1. Bureau of the Census
2. Bureau of Labor Statistics (BLS)
3. Bureau of Economic Analysis (BEA)

Front 53

econ base - census bureau

Back 53

• Aside from data on populationand housing,
the Census Bureau also collects data on
business activity.
• Business activity data describe by industry:
–Aggregate size of the industry (in terms of jobs or
value of shipments, for example)
–Number of companies and establishments
–Measure of business operation (cost of raw
materials; imports and exports)

Front 54

econ base - bureau of labor statistics

Back 54

1. Labor Force Data: the Local Area Unemployment
Statistics (LAUS) describes employment,
unemployment, and unemployment rate (monthly)
2. Job and Wage Data: At the place of work, through
several sources:
–Covered Employment and Wages (ES-202)
–Current Employment Statistics (CES)
3. Prices and Living Conditions Data: Consumer Price
Index (CPI); Consumer Expenditure Survey

Front 55

econ base - bureau of economic analysis

Back 55

• Part of the Department of Commerce,
produces one big, complex, integrated data
set.
• Plays the role of the nation’s economic
accountant.
• Data are more useful for sophisticated
regional data users:

Front 56

econ base - conceptualizing local econ

Back 56

• The most common theory planners use to
conceptualize and analyze the local economy
is Economic Base Analysis.
• The local economy is conceptualized as having
two sectors of activity:
–Basic Sector (aka Non-Local Sector)
–Non-Basic Sector(aka Local Sector)

Front 57

econ base - basic sector

Back 57

•(aka Non-local Sector): Consists of firms and
parts of firms whose economic activity is dependent upon
factors external to the local economy.
–Those industries that produce goods and services ultimately
exported to consumers outside the local region.
–Examples: Manufacturing, Agriculture, Forestry, State/Fed
Government

Front 58

econ base - non-basic sector

Back 58

•(aka Local Sector): Consists of firms and
parts of firms whose economic activity is dependent on
local economic conditions.
–Those industries that produce goods and services that are
consumed in the local region.
–Examples: Services (Restaurants, Drycleaners), Local
Government, Retail Trade

Front 59

econ base - 2 key assumptions about local econ

Back 59

• Basic sector is the primary cause of local economic growth
(i.e., the “economic base” of the local economy)
–A region’s export industries are it’s economic foundation, and all
other industries thrive by servicing the export industries and
one another.
–A change in the basic sector will lead automatically to a change
in the same direction in the non-basic sector (i.e., expansion or
decline in basic sector leads to overall expansion or decline).
• All local economic activities can be assigned to either the
basic or non-basic sector.

Front 60

econ base - base multiplier

Back 60

• One of the primary uses of the EB model is in
the calculation of a Base Multiplier.
• It tells how many total jobs are created from
the addition of one basic job.
• Can be used to project the effect on the total
economy from expected changes to the basic
sector.
• Defined as: Ratio of total economic activity to
basic economic activity.

Front 61

econ base - base multiplier calculation

Back 61

=total econ activity/basic econ activity
or if in terms of jobs
=total local employment/basic local employment

Front 62

econ base - 4 decisions when undertaking econ base analysis

Back 62

1) Area to be Studied (Geography)
2) Unit of Analysis (Measure of the Economy)
3) Data to be Used(Source for Input Data)
4) Technique(s) to be Used (Analytical Method(s))

Front 63

econ base - base analysis choosing unit of analysis

Back 63

• Employment: Number of jobs by industry. Pros: Most widely
used; Annual data widely available; Jobs are readily understood.
Cons: Dealing with part-time employees, seasonal jobs,
commuters; Changes in productivity are not considered.
• Payroll: Annual payroll for firms by industry. Pros: Reflects hours
worked and wage rates; Recognizes that all jobs are not equal;
Good data availability. Cons: Not as readily understood (as jobs);
Overemphasizes importance of income in the economy (e.g., one
$250K job vs. nine $25K jobs)
• Sales: Dollar sales by industry. Pros: Easily understood by firms
and government; Tax revenues are related to sales. Cons: Can
have double-counting; Poor data availability.
• Value Added: Like sales, but eliminates double-counting by
subtracting a firm’s purchases from their sales. Pros: Eliminates
the double-counting problem. Cons: Very poor data availability.

Front 64

econ base - base analysis selecting data set

Back 64

County Business Patterns. Pros: Available annually (but with
three-year lag); widely used; includes employment, payroll, and
sales. Cons: Derived from a combination of sources; does not
include government employment.
Economic Census: Pros: More reliable than CBP; contains
employment, payroll, and sales; available at zip code level. Cons:
Only collected every five years, and not available until several
years later.
ES202 Data: Pros: Available annually and by quarter; quick data
turnaround; includes employment and payroll; can be aggregated
at small geographies. Cons: Not readily available; Does not
include non-unemployment compensated firms.

Front 65

econ base - base analysis choosing technique

Back 65

• Survey Approach:Conduct a survey to measure the size of the flow of
resources into and out of the region.
• Assumption Approach: The simplest and most straightforward, assumes
certain industries are Basic or Non-basic. (e.g., agricultural, mining,
manufacturing, and federal government are usually assigned to basic
sector)
• Judgment Approach: Requires deep and broad local knowledge.
(Groceries and dentists –surely local; Printing company –local paper or
encyclopedias?)
• Minimum Requirements Approach: Compares local industry patterns with
other similar areas to determine the minimum level of NB employment.
• Regression Approach: Multivariate prediction model for each sector.
• Location Quotient Approach: Related to the concentration concept, this
technique determines the local share of an industry. Assumes all LQs
above 1 are export.

Front 66

econ base - location quotient approach

Back 66

•Location quotient: compares the local share of a
given industry to the share of that industry for a
larger area. It measures the relative specialization of
an industry in the local economy compared to the
larger area (often the nation).
•Defined as the ratio of an industry’s share of the local
economy to the industry’s share of the larger
economy.

Front 67

econ base - location quotient equation

Back 67

(ei/et)/(Ei/Et)

where:
ei = Local employment in sector i;
eT= Total local employment;
Ei = National employment in sector i;
ET= Total national employment.
(All in the same year)

Front 68

econ base - evaluating lq values

Back 68

lq=1, self sufficiency
lq>1, net exports
lq<1, net imports

With an LQ > 1, that proportion of the industry that accounts for this excess
production is considered basic.

Front 69

econ base - calculating basic employment with lq values

Back 69

=((ei/Ei - et/Et) Ei

Front 70

economic base - strengths

Back 70

• Easy to understand (and communicate).
• Easy application.

Front 71

econ base - critiques

Back 71

• Exclusively relies on exports as the determinant of economic change.
Misses other important factors.
– Y=C+I+G+(E-M): total regional income (Y) is equal to consumption (C) +
investment (I) + government expenditures (G) + the difference
between exports (E) and imports (M).
– EB theory assumes that consumption, investment, and government
expenditures are relatively constant across regions and across time
(may be reasonable for short time periods but certainly not for long
ones).
• Fails to distinguish between the linkages of different export activities. Not
all export industries influence the local economy in the same magnitude
through linkages.
• “Chicken and egg” issues: In some areas, nonbasicactivity can be an
important precondition to the development of basic industry, i.e., the
basic may be induced by the nonbasicin addition to vice versa. For
example, housing construction—typically viewed as a nonbasicactivity—
can build a particular kind of labor force.

Front 72

econ base - troublesome assumptions of lq approach

Back 72

1. Labor productivity does not vary geographically. - underestimate basic employment
2. Patterns of consumption do not vary
geographically. - overestimate basic employment

3. No international trade. - overestimate basic employment

4. No cross-hauling. - underestimate basic employment

Front 73

econ base analysis works best when

Back 73

when focused
on short-term changes on small-scale economies.

Front 74

econ base - multiplier effect

Back 74

• The total effect on the economy (typically
measured in number of jobs) resulting from a
change in the basic sector.
• Multiply the basic jobs by the BM and then
deduct the basic employment since the “total
effect" should not include the original change.

Front 75

econ base - shift/share analysis overview

Back 75

why is this reason changing?
taking into account - national total trends, industry trends, local advantages/disadvantages

Front 76

econ base - shift/share analysis components

Back 76

• Economic Growth Factor
Measures the aggregate employment changes in the larger, reference
economy.
• Proportional Shift
How much growth in an industry is an effect of the changing fortune of
the industry nationwide, above and beyond (or less than) the rising or
falling national economy? This component tells us the relative change
due to the industrial mix effect. If an industry is growing more rapidly
than the economy as a whole, this factor will be positive.
• Differential Shift
How much of the change in the local economy is due to extraordinary
local conditions? The differential shift represents the local economy’s
relative advantage (or disadvantage) in a particular industry during the
time in question.

Front 77

economic base - strengths

Back 77

• Easy to understand (and communicate).
• Easy application.

Front 78

econ base - critiques

Back 78

• Exclusively relies on exports as the determinant of economic change.
Misses other important factors.
– Y=C+I+G+(E-M): total regional income (Y) is equal to consumption (C) +
investment (I) + government expenditures (G) + the difference
between exports (E) and imports (M).
– EB theory assumes that consumption, investment, and government
expenditures are relatively constant across regions and across time
(may be reasonable for short time periods but certainly not for long
ones).
• Fails to distinguish between the linkages of different export activities. Not
all export industries influence the local economy in the same magnitude
through linkages.
• “Chicken and egg” issues: In some areas, nonbasicactivity can be an
important precondition to the development of basic industry, i.e., the
basic may be induced by the nonbasicin addition to vice versa. For
example, housing construction—typically viewed as a nonbasicactivity—
can build a particular kind of labor force.

Front 79

econ base - troublesome assumptions of lq approach

Back 79

1. Labor productivity does not vary geographically. - underestimate basic employment
2. Patterns of consumption do not vary
geographically. - overestimate basic employment

3. No international trade. - overestimate basic employment

4. No cross-hauling. - underestimate basic employment

Front 80

econ base analysis works best when

Back 80

when focused
on short-term changes on small-scale economies.

Front 81

econ base - multiplier effect

Back 81

• The total effect on the economy (typically
measured in number of jobs) resulting from a
change in the basic sector.
• Multiply the basic jobs by the BM and then
deduct the basic employment since the “total
effect" should not include the original change.

Front 82

econ base - shift/share analysis overview

Back 82

why is this reason changing?
taking into account - national total trends, industry trends, local advantages/disadvantages

Front 83

Susan B. Anthony

Back 83

American civil rights leader
75 to 100 speeches every year on women's rights for 45 years.
Weekly journal called the revolution. Was publisher and business manager while Shanton was the editor

Hint 83

100 speeches

Front 84

Measuring Inequality:
What is a dissimilarity index?

Back 84

measure of the evenness with which 2 groups are distributed across the zones that make up a larger region

Front 85

Measuring Inequality:
How do you interpret the dissimilarity index?

Back 85

the percentage of one of the 2 groups that would have to move to different zones to produce a completely even distribution

Front 86

Measuring Inequality:
What is the key assumption behind dissimilarity index?

Back 86

The ideal distribution is one where any zone's distribution mirrors that of the region

Front 87

Measuring Inequality:
What is a Lorenz Curve?

Back 87

measure of the extent to which some indicator is equally distributed among a population (ex: income)

Front 88

Measuring Inequality:
How do you interpret a Lorenz Curve?

Back 88

the further the LC lies below the line of equality, the more unequal the distribution is

Front 89

Measuring Inequality:
What is a Gini Coefficient?

Back 89

converts the LC into a single numeric measure, between 0 and 1

Front 90

Measuring Inequality:
How do you interpret the Gini Coefficient?

Back 90

0= perfect equality
1= absolute inequality
lower GC = more equal distribution