CONTEXT

CURRENT DEMOGRAPHICS & 

GROWTH FORECAST 

ADOPTED | APRIL 2016

APPENDIX

SOUTHERN CALIFORNIA ASSOCIATION OF GOVERNMENTS
INTRODUCTION 1

FORECASTING PROCESS 1

GROWTH TRENDS 2

REGIONAL GROWTH FORECAST 12

FORECAST METHODOLOGY AND ASSUMPTIONS 16

SMALL AREA FORECAST AND ALLOCATION 21

REFERENCES 43

ADOPTED | APRIL 2016

APPENDIX

CURRENT CONTEXT I DEMOGRAPHICS & GROWTH FORECAST
DEMOGRAPHICS & GROWTH FORECAST 

INTRODUCTION

The Regional Growth Forecast is used as a key guide for developing regional plans and 

strategies mandated by federal and state governments such as the Regional Transportation 

Plan/Sustainable Communities Strategy (RTP/SCS), the Program Environmental 

Impact Report (PEIR) for the RTP/SCS, the Air Quality Management Plan (AQMP), 

the Federal Transportation Improvement Program (FTIP), and the Regional Housing 

Needs Assessment (RHNA).

The Growth Forecast Appendix to 2016 RTP/SCS is intended to provide more details on the 

development of the regional growth forecasts for the 2016 RTP/SCS. The Growth Forecast 

Appendix is comprised of five major sections. Section I summarizes the forecasting process 

focusing on the forecasting timeline and milestones. Section II provides an overview of the 

recent trends in the region’s growth of population, households, and employment. Section 

III explores the regional growth forecast with its socio-economic characteristics. Section 

IV discusses the forecast methodology and the major assumptions for the regional growth 

forecast. Section V describes the small area forecast and allocation.

FORECASTING PROCESS

The regional growth forecast reflects recent and past trends; key demographic and economic 

assumptions; and local, regional, state or national policies. The SCAG’s regional growth 

forecast also emphasizes the participation of local jurisdictions and other stakeholders in the 

growth forecast development process. TABLE 1 lists the forecasting timeline and milestones 

for the development of the regional growth forecast for the 2016 RTP/SCS. 

The first major milestone for the growth forecast development was the SCAG panel of 

demographic and economic experts meeting. On June 27, 2013, the SCAG panel of 

demographic and economic experts meeting was held to review SCAG’s methodology and 

assumptions for its population, household, and employment growth forecast for the 2016 

RTP/SCS. Twenty academic scholars and leading practitioners were invited to participate on 

the panel. The panel of experts reviewed demographic and economic trends in the national 

and regional growth context, discussed the key assumptions underlying the regional and 

county growth forecast, and provided responses to survey questions on major assumptions 

(see (1) 2016 RTP/SCS) growth forecast development: information from panel of experts 

meeting and range of regional growth projections at http://www.scag.ca.gov/committees/

CommitteeDocLibrary/cehd080113fullagn_3.pdf; (2) panel survey results and tabulation at 

http://www.scag.ca.gov/Documents/SurveyAnswersSummary062713.pdf).

On September 12, 2013, SCAG incorporated the recommendations of the panel of experts 

into the refined range of regional growth forecasts and developed a recommended, 

preliminary set of regional and county growth forecasts for 2012, 2020, 2035 and 2040, 

reflecting recent trends and updated assumptions. 

In November 2013, the preliminary small area (e.g., jurisdiction and transportation analysis 

zone) growth forecasts, reflecting recent trends and controlling for the updated county 

controls, were released to local jurisdictions for their comments and input in November 

2013. SCAG provided local jurisdictions with the preliminary set of growth forecasts at the 

jurisdiction and transportation analysis zone levels.

Between February 2014 and October 2014, SCAG conducted the first round of local review 

through one-on-one meetings with local jurisdictions. As with the 2012 RTP/SCS, SCAG 

sought verification of the existing land use, general plan land use, and zoning information; 

and approval of jurisdictional level population, households and employment forecasts for 

the years 2020, 2035 and 2040. Jurisdictions were allowed to submit sub-jurisdictional 

input (e.g., input at the census tract or transportation analysis zone level). However, the 

sub-jurisdictional level input would only be treated as advisory. SCAG held one-on-one 

meetings with 195 of the 197 local jurisdictions in the SCAG region to explain the methods 

and assumptions of how the small area growth forecasts were developed. The local 

jurisdictions provided SCAG with their input on those growth forecasts along with the proper 

documentation by end of September 2014. SCAG updated the local growth forecasts and 

revised them as necessary. 

There was a SCAG staff assessment of the draft local input growth forecast in September 

2014 (http://www.scag.ca.gov/committees/CommitteeDocLibrary/cehd100214fullagn.pdf). 

As of September 2014, 81 percent of 197 jurisdictions provided input on SCAG’s preliminary 

growth forecasts. The key findings from the aggregated local input data included: 1) All three 

(3) growth figures were within the preliminary range of growth forecasts; 2) All three growth 

figures from local jurisdictions were lower than the preliminary mid forecasts, but higher 

than the preliminary low forecasts, in 2040; and 3) the 2040 regional unemployment rate 

would be 5.4 percent for the SCAG region. The population to household ratio was 3.0 and is 

consistent with that of the preliminary growth forecasts. The local input growth forecast at 

the regional level was found to be technically sound. The local input was primarily existing 

general plan-based. 

In November 2014, SCAG produced the draft small area (e.g., jurisdiction and transportation 

analysis zone) growth forecasts reflecting local input, and further developed the alternative 

growth forecasts reflecting different land use scenarios (trend baseline and three policy 

scenarios) in subsequent months. As part of the scenario planning exercise, SCAG 

developed a policy growth scenario. The goal of this scenario is to maximize the benefits of 
 2 2016–2040 RTP/SCS I APPENDIX

GROWTH TRENDS

POPULATION

According to the 2015 population estimates from the California Department of Finance, the 

population of the Southern California region is 18.9 million, which represents 5.9 percent 

of the 321 million people of the U.S., and 48.3 percent of California’s population. With the 

region’s land area of 38,000 square miles, the region’s population density is now 490 

people per square mile. The Southern California region is the 5th highest in population among 

states in the nation, behind New York and ahead of Illinois, and the second largest combined 

statistical area (CSA) in the nation behind the New York CSA.

Greenhouse Gas/Vehicle Miles Traveled (GHG/VMT) reductions, public health, and other 

co-benefits from the large transportation investments in our region focusing on transit and 

first/last mile strategies. This is done by identifying opportunity areas with current and/or 

future transit investments where mixed use and high density housing are mostly likely to 

occur in the future.

Between June 2015 and July 2015, SCAG conducted a second and final round 

of local review of both the draft local input and draft policy growth forecasts. All 

the comments received were incorporated into the draft 2016 RTP/SCS to ensure 

accuracy and reasonableness.

After developing the draft 2016 RTP/SCS between August 2015 and November 2015, SCAG 

released the draft Plan in December 2015. The Regional Council is scheduled to adopt the 

2016 RTP/SCS in April 2016. 

Table 1 Forecasting Timeline and Milestones

Milestone Date/Period Reference Materials

1 Adopted the 2012 RTP/SCS jurisdictional level growth forecast. April 2012 http://gisdata.scag.ca.gov/Pages/SocioEconomicLibrary.aspx

2 Developed an initial range of preliminary 2016-2040 RTP/SCS regional growth 

forecast with major demographic and economic assumptions. June 2013 http://www.scag.ca.gov/committees/CommitteeDocLibrary/

cehd080113fullagn.pdf

3

Held a panel of experts meeting to assess U.S.Bureau of Labor Statistics (BLS), 

U.S. Census Bureau, and California Department of Finance (DOF) projections 

and to discuss demographic and economic trends and assumptions.

June 2013 http://www.scag.ca.gov/committees/CommitteeDocLibrary/

cehd080113fullagn.pdf

4 Developed a recommended preliminary set of region and county growth forecasts. September 2013 http://www.scag.ca.gov/committees/CommitteeDocLibrary/

cehd080113fullagn.pdf

5 Developed the initial draft of the small area forecast at jurisdiction/TAZ level and 

released it to local jurisdictions for comments. November 2013

6 Started the one-on-one meeting with the local jurisdictions for local review across 

the region. February 2014-October 2014 http://www.scag.ca.gov/committees/CommitteeDocLibrary/

cehd020614fullagn.pdf

7 Released preliminary draft local input/general plan growth forecast. November 2014 http://www.scag.ca.gov/

Documents/2016DraftGrowthForecastByJurisdiction.pdf

8 Released both draft local input/general plan growth forecast and the draft policy 

growth forecast for 2016-2040 RTP/SCS for comments and local input. June 2015-July 2015

9 Release of the draft 2016-2040 RTP/SCS. December 2015

10 Scheduled adoption of the 2016-2040 RTP/SCS. April 2016
CURRENT CONTEXT I DEMOGRAPHICS & GROWTH FORECAST 3

The recent population growth of the region from 2010-2015 is an extension of the existing 

slow growth pattern observed during the 2000-2010 period (see TABLE 2). Although the 

regional economy has recovered from the Great Recession by adding 800,000 jobs with 

the lower unemployment rate, the regional population continues to show slow growth. The 

average annual growth rate for the 2010-2015 period was only 0.8 percent, which was lower 

than the 0.9 percent growth rate of the 2000-2010 period. 

California and the U.S. have also experienced slow growth over the last 15 years, which 

will continue over the next 25 years. The average annual growth rate of the SCAG region, 

California, and the U.S. during the 2015-2040 period is consistent with or lower than the 

growth rate for the 2010-2015 period.

The Great Recession had a significant impact on the regional population growth. The Great 

Recession resulted in the lowest number (75,000) and the lowest percent change (0.4 

percent) in the 2008-2009 annual population growth of the Southern California region 

since 2000. The number and the percent change in the annual population growth after the 

Great Recession has steadily increased, up to 144,000 and 0.7 percent in 2014-2015 (see 

FIGURE 1 and FIGURE 2). The post-recession growth was much lower than that of 2000-

2005: the annual growth and the average percent change of population were 144,000 and 

0.7 percent in the in the 2010-2015 period, while population growth and the average percent 

change of population in the 2000-2005 period were 200,000 and 1.2 percent. 

The region’s population growth is mainly determined by two major components: natural 

increase (births-deaths) and net migration (net domestic migration and net immigration) (see 

FIGURE 3). There was a significant change in net domestic migration and net immigration 

after the Great Recession (see FIGURE 4 and FIGURE 5). During the 2007-2010 period, more 

people moved out of the region than into the region. The average annual number of outmigrants from the region was 150,000 people more than that of in-migrants from the other 

parts of the country. During the same period, 83,000 legal immigrants and undocumented 

immigrants from foreign countries immigrated annually to the region. However, only 60,000 

people annually left the region for other parts of the nation and 63,000 people immigrated 

annually to the region between 2010-2015. 

Table 2 Average Annual Growth Rate of Population, 2000-2040

2000-2010 2010-2015 2015-2040

SCAG Region 0.9% 0.8% 0.7%

California 1.0% 1.0% 0.9%

United States 1.0% 0.8% 0.8%

Source: U.S. Census Bureau, CA DOF, SCAG

15.5

16.0

16.5

17.0

17.5

18.0

18.5

19.0

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012201320142015

In Millions

Year

Figure 1 Population Growth, SCAG Region, 2000-2015 (in Millions)

Source: CA DOF, SCAG Annual Percent Change

0.0%

0.2%

0.4%

0.6%

0.8%

1.0%

1.2%

1.4%

1.6%

00-01

01-02

02-0303-0404-05

05-06

06-07

07-08

08-09

09-10

10-1111-12

12-1313-1414-15

Figure 2 Annual Percent Change of Population, SCAG Region, 2000-2015

Source: CA DOF, SCAG
 4 2016–2040 RTP/SCS I APPENDIX

-100

-50

0

50

100

150

200

250

300

00-0101-0202-0303-0404-0505-0606-0707-0808-0909-1010-1111-1212-13

13-1414-15

Natural Increase Net Migration

In Thousands

Figure 3 Components of Population Change, SCAG Region, 2000-2015 (in Thousands)

Source: CA DOF, SCAG

In Thousands

-180

-140

-100

-60

-20

20

00-0101-02

02-0303-0404-05

05-06

06-0707-08

08-09

09-10

10-11

11-12

12-1313-1414-15

Figure 4 Net Domestic Migration, SCAG Region, 2000-2015 (jn Thousands)

Source: CA DOF, SCAG

Although more migrants have come into this region after the Great Recession, the number 

of births has continued to decline in recent years. The average annual number of births 

decreased from 266,000 during the 2007-2010 period to 243,000 during the 2010-

2015 period (see FIGURE 6). During the same period, the total fertility rate decreased from 

2.1 to 1.9. Whether the fertility rate in the future will continue to decline is a challenging 

question for demographers.

With changing components (births, domestic migration, immigration) to population growth 

since 2010, the demographic characteristics of the regional population changed accordingly 

(see TABLE 3). First, the region’s population has become older. The median age increased 

from 34.6 in 2010 to 35.4 in 2015. The percentage of the population 65 years old and over 

increased from 10.9 percent in 2010 to 12.3 percent in 2015, while the percentage of the 

working-age population of 16-64 years old decreased slightly from 66.7 percent in 2010 to 

66.3 percent in 2015. As a result, the old-age dependency ratio increased from 16.4 in 2010 

to 18.5 in 2015 by 2.1. The old-age dependency ratio is defined as the ratio of those 65 years 

old or more to the working-age population—those ages 16-64. It is usually measured as the 

proportion of dependents per 100 working-age population. 

In Thousands

0

20

40

60

80

100

120

00-0101-02

02-0303-0404-05

05-06

06-07

07-08

08-09

09-10

10-1111-12

12-1313-1414-15

Figure 5 Net Immigration, SCAG Region, 2000-2015 (in Thousands)

Source: CA DOF, SCAG
CURRENT CONTEXT I DEMOGRAPHICS & GROWTH FORECAST 5

Table 3 Demographic Characteristics of Regional Population, 2000-2040

2000 2010 2015 2040 Difference (2010-2015) Difference (2015-2040)

Total population (in Thousands) 16,574 18,075 18,779 22,138 700 (Annual Average % 

Change: 0.8%)

3,559 (Annual Average % 

Change: 0.7%)

Annual Natural Increase (00-10, 10-15, 35-40) 163,260 136,805 110,715 -26,455 -26,090

Annual Births (00-10, 10-15, 35-40) 270,283 247,101 274,493 -23,182 27,392

Annual Deaths (00-10, 10-15, 35-40) 107,023 110,296 163,778 3,273 53,482

Annual Net Migration (00-10, 10-15, 35-40) -13,128 3,828 15,043 16,956 11,000

Annual Net Immigration 81,628 62,941 95,950 -18,687 33,000

Annual Net Domestic Migration -94,756 -59,114 -89,907 35,642 -30,000

Components of Population Growth (00-10, 10-15, 35-40)

Natural Increase 108.7% 99.8% 87.9% -8.9% -11.9%

Net Migration -8.7% 0.2% 12.1% 8.9% 11.9%

Total 100.0% 100.0% 100.0%

Age Composition of Population

Persons Under 16 Years Old 25.6% 22.4% 21.4% 19.3% -1.0% -2.1%

Persons 16–64 Years Old 64.4% 66.7% 66.3% 62.5% -0.4% -3.8%

Persons 65 Years Old And Over 9.9% 10.9% 12.3% 18.2% 1.4% 5.9%

Total 100.0% 100.0% 100.0% 100.0%

 Median Age

Male 31.3 33.4 34.3 36.9 0.9 2.6

Female 33.3 35.7 36.5 38.9 0.7 2.4

Total 32.3 34.6 35.4 37.9 0.8 2.5

Dependency Ratio

Child Dependency Ratio* 39.8 33.6 32.3 28.0 -1.3 -4.3

Old-Age Dependency Ratio** 15.4 16.4 18.5 28.2 2.1 9.7

Total Dependency Ratio*** 55.2 50.0 50.8 56.2 0.8 5.4

Ethnic Composition of Population

White (NH) 39.6% 33.6% 31.4% 22.4% -2.2% -9.0%

Black (NH) 7.4% 6.6% 6.3% 5.4% -0.3% -0.8%

Asian & Others (NH) 12.5% 14.6% 15.6% 19.1% 1.0% 3.5%

Hispanic 40.6% 45.3% 46.7% 53.1% 1.4% 6.4%

Total 100.0% 100.0% 100.0% 100.0%

Entropy Index (Normalized)**** 0.860 0.856 0.853 0.826 -0.003 -0.027

Note: * The number of children per hundred people of working age. ** The number of seniors (65+) per hundred people of working age. ***The number of children (age 0-15) and aged persons (age 65 and over) per hundred people of working age (age 16–64). 

**** The enropy index (normalized) ranges from 0 (less diverse) to 1 (more diverse). NH - Non-Hispanic. Source: U.S. Census Bureau, CA DOF, SCAG
 6 2016–2040 RTP/SCS I APPENDIX In Thousands

210

220

230

240

250

260

270

280

290

00-01

01-02

02-03

03-04

04-05

05-06

06-07

07-08

08-09

09-10

10-11

11-12

12-13

13-14

14-15

Figure 6 Births, SCAG Region, 2000-2015 (in Thousands)

Source: CA DOF, SCAG

5.2

5.3

5.4

5.5

5.6

5.7

5.8

5.9

6.0

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

Year

In Millions

Figure 7 Household Growth, SCAG Region, 2000-2015 (in Millions)

Source: CA DOF, SCAG Annual Percent Change

0.0%

0.2%

0.4%

0.6%

0.8%

1.0%

1.2%

1.4%

00-01

01-02

02-03

03-04

04-05

05-06

06-07

07-08

08-09

09-10

10-11

11-12

12-13

13-14

14-15

Figure 8 Percent Change of Households, SCAG Region, 2000-2015

Source: CA DOF, SCAG

Second, the region is currently one of the most ethnically diverse regions in the nation. 

Hispanic and NH Asian/Other populations increased their share from 59.9 percent in 2010 

to 62.3 percent in 2015, while NH White and NH Black populations decreased their share 

from 40.2 percent in 2010 to 37.7 percent in 2015. The region’s ethnic composition has 

moved toward bigger Hispanic and Asian/Other populations and smaller White and Black 

populations over time. The ethnic diversity remains high. The normalized entropy index 

was used to measure the ethnic diversity. The normalized entropy index ranges from 0 to 

1, and approaches its maximum of 1 when four race/ethnic groups are equally present. The 

normalized entropy index for the region was 0.855 in 2010 and 0.853 in 2015. The region 

already reached the highest entropy index of 0.858 in 2001, and was higher than the nation. 

The normalized entropy index for the nation was 0.748 in 2000. 

HOUSEHOLDS

The Great Recession had a more significant impact on regional household growth than 

population growth. Only 100,000 households (20,000 households per year) were added 

to the region during the 2010-2015 period, while 800,000 people (150,000 people per 

year) were added to the region during the 2010-2015 period (see FIGURE 7). The slower 

household growth could be explained in part by demographic factors, including population 

growth, age composition of population, and household formation. Natural increase was a 

key driving force of the recent population growth. Additionally, most of the immigrants were 

of Asian and Hispanic population who were showing a lower household formation level. 
CURRENT CONTEXT I DEMOGRAPHICS & GROWTH FORECAST 7

rates by sex also extended their historical trends between 2010-2015. During the 2010-

2015 period, the male headship rates continued to decline, while the female headship rates 

continued to increase. The NH Asian/Other headship rates increased slightly, while the other 

racial/ethnic headship rates declined during the 2010-2015 period. (See TABLE 10).

The housing shortage is another major factor contributing to the slower household growth. 

The housing supply was sluggish from 2010-2014, although there was an increasing pattern 

of housing production on an annual basis. In 2014, 40,000 residential building permits were 

approved (see FIGURE 10). In particular, the permits for multiple housing units account for 

over 60 percent of total residential building permits from 2010-2015. The share of multiple 

housing permits in the most recent five years is much higher than that of 2000-2010. 

EMPLOYMENT

After losing 800,000 jobs between 2007 and 2010, the SCAG region has returned to the 

pre-recession level of eight million jobs in 2015 with a much lower unemployment rate of 6.6 

percent in 2015 than 12.3 percent in 2010 (see FIGURE 11 and FIGURE 12). In order to achieve 

the pre-recession level of jobs, the region needed to add the jobs at an annual growth rate 

of 2.1 percent starting in 2010 (see FIGURE 13). The regional share of national jobs increased 

from 5.1 percent in 2010 to 5.3 percent in 2015. The changing unemployment rate is directly 

correlated with the change of the population-employment (P-E) ratio. The P-E ratio is high 

The annual average growth rate of households was only 0.3 percent from 2010-2015 (see

FIGURE 8). The average household size increased from 3.0 in 2010 to 3.1 in 2015 due to the 

growth of NH Asian/Other and Hispanic groups (see FIGURE 9). The average household size 

of Hispanics increased from 4.0 in 2010 to 4.1 in 2015, and average household size of Asian 

and Others increased from 3.1 in 2010 to 3.2 in 2015. 

The age and racial/ethnic composition of householders changed according to the changing 

demographic characteristics of the population between 2010 and 2015. Householders 

are getting older and remain diverse in 2015. Householders who were 55 years and older 

increased their share from 35.8 percent in 2010 to 40.2 percent in 2015, while householders 

who were 15-54 years old decreased their share from 64.2 percent in 2010 to 59.8 percent 

in 2015. Householders of Hispanics and NH Asian/Others increased their share from 48.2 

percent in 2010 to 50.6 percent in 2015, NH White and NH Black Householders decreased 

their share from 51.7 percent in 2010 to 49.4 percent in 2015 (see TABLE 4). 

The overall headship rates (the number of people 15 years old and over who are counted 

as heads of households divided by the number of people 15 years old and over) measuring 

household formation behavior have declined from 41.3 percent in 2010 to 40.3 percent in 

2015. The headship rates continued to decline in the 2010-2015 period as well as between 

2000-2010. The greatest decline in the headship rate between 2010-2015 was observed in 

the 75+ age group (-3.1 percentage points), while the 25-34 age group showed the greatest 

decline in the headship rate between 2000-2010 (-3.9 percentage points). The headship 

Source: Construction Industry Research Board Building Permits (In Thousands)

0

10

20

30

40

50

60

70

80

90

100

Single Family Multi Family

00-01

01-0202-03

03-04

04-0505-0606-0707-08

08-09

09-10

10-11

11-12

12-1313-14

14-15

Figure 10 Residential Building Permits Issued by Housing Types, SCAG Region, 2000-2015 (in 

Thousands)

3.00

3.02

3.04

3.06

3.08

3.10

3.12

2000

2001

2002

2003

20042005

2006

2007

2008

2009

2010

2011

2012201320142015

Average Household Size

Year

Figure 9 Average Household Size, SCAG Region, 2000-2015

Source: CA DOF, SCAG
 8 2016–2040 RTP/SCS I APPENDIX

2000 2010 2015 2040 Difference (2010-2015) Difference (2015-2040)

Total Households (in Thousands) 5,400 5,848 5,947 7,412 99 (Annual Average % 

Change: 0.3%)

1,465 (Annual Average % 

Change: 1.0%)

AGE COMPOSITION OF HOUSEHOLDERS 

15-24 4.3% 3.9% 2.9% 2.4% -0.9% -0.5%

25-34 19.2% 16.4% 15.6% 13.1% -0.2% -2.5%

35-44 25.6% 21.3% 19.8% 18.1% -1.3% -1.8%

45-54 20.3% 22.7% 21.5% 18.7% -1.4% -2.8%

55-64 12.6% 17.0% 18.6% 16.2% 1.4% -2.4%

65-74 9.5% 10.1% 12.4% 14.8% 1.9% 2.5%

75+ 8.6% 8.7% 9.2% 16.8% 0.5% 7.6%

Total 100.0% 100.0% 100.0% 100.0% ETHNIC COMPOSITION OF HOUSEHOLDERS

White (NH) 49.4% 43.7% 41.9% 28.9% -2.2% -13.0%

Black (NH) 8.1% 8.0% 7.5% 6.6% -0.6% -0.9%

Asian & Others (NH) 12.7% 13.7% 15.1% 20.1% 1.8% 5.0%

Hispanic 28.8% 34.5% 35.5% 44.4% 1.2% 8.9%

Total 100.0% 100.0% 100.0% 100.0% AVERAGE HOUSEHOLD SIZE

White (NH) 2.4 2.3 2.3 2.3 0.0 0.0

Black (NH) 2.7 2.6 2.6 2.5 0.0 -0.1

Asian & Others (NH) 3.2 3.1 3.2 2.8 0.1 -0.4

Hispanic 4.3 4.0 4.1 3.6 0.1 -0.5

Total 3.0 3.0 3.1 3.0 0.1 -0.1

Table 4 Characteristics of Regional Households, 2000-2040

Note: NH - Non-Hispanic. Source: U.S. Census Bureau, CA DOF, SCAG
CURRENT CONTEXT I DEMOGRAPHICS & GROWTH FORECAST 9

0%

2%

4%

6%

8%

10%

12%

14%

2000

2001

2002

200320042005

2006

2007

2008

2009

2010

2011

2012

2013

20142015

Year

Unemployment Rate

Figure 11 Unemployment Rate, SCAG Region, 2000-2015

Source: CA EDD, SCAG

6.0

6.5

7.0

7.5

8.0

8.5

9.0

2000

2001

2002

2003

2004

2005

2006

2007

20082009

2010

2011

2012201320142015

In Millions

Year

Figure 12 Job Growth, SCAG Region, 2000-2015 (in Millions)

Source: CA EDD, SCAG

Annual Percent Change

-8%

-6%

-4%

-2%

0%

2%

4% 00-01

01-02

02-0303-0404-0505-0606-0707-08

08-09

09-10

10-1111-12

12-1313-1414-15

Figure 13 Annual Percent Change of Jobs, SCAG Region, 2000-2015

Source: CA EDD, SCAG

2.0

2.1

2.2

2.3

2.4

2.5

2000

2001

2002200320042005

2006

2007

20082009

2010

2011

2012

201320142015

Year

Population to Employment Ratio

Figure 14 Population to Employment Ratio, SCAG Region, 2000-2015

Source: CA DOF, CA EDD, SCAG
 10 2016–2040 RTP/SCS I APPENDIX

population and employment distribution using the county in the region as a unit of analysis. 

If HIOC equals 0, then population and employment are perfectly deconcentrated across 

the region. If HIOC equals 100, then population and employment are concentrated into one 

county in the region. Considering the suburbanization of population and employment in the 

region, the historical pattern of the HIOC tends to move toward the lower level, which means 

more deconcentration. The SCAG region showed a downward trend of HIOC from 62.09 in 

2000 to 58.34 in 2010 to 58.19 in 2015 for population, and from 67.41 in 2000 to 64.91 in 

2010 to 63.43 in 2015 for employment. The suburbanization of population and employment 

(in particular, population) in the post-recession period has slowed down compared to 

the 2000-2010 period. 

While there has been a downward change in HIOC along with spatial changes in population 

and employment in the region, the gap of HIOC between population and employment 

became smaller after the recession compared to the 2000-2010 period. The Index 

of Divergence (IOD) was used to measure the gap of HIOC between population and 

employment. If IOD equals 0, then there is no gap between two HIOCs. This means that 

the county distribution of both population and employment is more balanced and there 

is a convergence of the county distribution of population and employment. For example, 

the share of both Riverside and San Bernardino Counties’ population increased from 23.4 

percent in 2010 to 23.5 percent in 2015 by 0.1 percent, while the share of both Riverside and 

San Bernardino Counties’ employment increased from 17.2 percent in 2010 to 18.4 percent 

in 2015 by 1.2 percent. The county distribution of population and employment indicates that 

faster growth of employment in Riverside and San Bernardino Counties, and Imperial County 

reduced the gap in the suburbanization level of population and employment observed in 

2010. The IOD decreased from 0.066 in 2010 to 0.052 in 2015. This change will have a 

positive implication for regional transportation and air quality. 

The population to employment (P-E) ratio was used to measure the balance of county 

population and employment. All counties in the region experienced a decline in P-E ratio 

between 2007 and 2010. The regional P-E ratio declined from 2.5 to 2.3 during the same 

period. Riverside, Imperial, and San Bernardino Counties experienced a faster decline in the 

P-E ratio than other counties: 3.7 in 2010 to 3.1 in 2015 for Riverside County; 3.1 in 2010 to 

2.4 in 2015 for Imperial County; 3.1 in 2010 to 2.9 in 2015 for San Bernardino County. 

in a recession, while it is low in a better business cycle. The P-E ratio was highest at 2.5 in 

2010, and moved toward the lower level of 2.4 in 2015. 

The Great Recession greatly influenced all of the industrial sectors and contributed to 

the fast restructuring of the industrial sectors (see TABLE 5). Eighteen major industrial 

sectors experienced a loss of jobs from 2007-2010 due to the Great Recession. Only 

two major industrial sectors did not experience a loss: accommodation and food service, 

public administration. Both the construction and manufacturing sectors were heavily 

impacted during the Great Recession and accounted for 40 percent of the total job losses 

that occurred during the Great Recession. The construction sector lost 170,000 jobs (36 

percent of 470,000 in 2007) and the manufacturing sector lost 150,000 jobs (18 percent of 

810,000 jobs in 2007). Other heavily impacted sectors during the same period were: retail 

trade (–100,000 jobs); administrative and support services (–90,000 jobs); professional, 

scientific and technical services (–70,000 jobs); and finance and insurance (–60,000 jobs). 

The wage level of seriously impacted industrial sectors (i.e., construction, manufacturing, 

the professional, scientific and technical services sectors) was relatively high. With such 

a loss in the higher wage sector jobs, the economic quality of the region’s residents was 

negatively impacted. 

Two industrial sectors accounted for nearly 30 percent of the total job growth (800,000) 

from 2010-2015: health care and social assistance (+130,000 jobs) and professional, 

scientific, and technical services (+100,000 jobs). Other industrial sectors adding a 

significant number of jobs were: accommodation and food service (+80,000); construction 

(+70,000 jobs); retail trade (+70,000 jobs); and administrative and support service and 

waste service (+70,000 jobs).

As a result of job growth by industrial sectors, the industrial structure remained serviceoriented. There was an increase in the share of professional, scientific, and technical services 

from 6.6 percent to 7.2 percent, followed by health care from 12.3 percent to 12.8 percent, 

and construction from 4.2 percent to 4.6 percent. There was also a decline in the share of 

industrial sectors including manufacturing from 9.1 percent in 2010 to 8.4 percent in 2015, 

and educational services from 9.5 percent in 2010 to 8.9 percent in 2015.

SUBURBAN GROWTH

The region continued its slow population growth in the post-recession period (2010-15), 

adding only 800,000 people, while experiencing rapid employment growth, adding 

800,000 jobs since 2010. Although suburbanization of population and employment 

continued, there was a little change in the county distribution of the regional population 

and employment during the same period (see TABLE 8). The Hoover Index of Concentration 

(HIOC; Plane and Rogerson, 1994) was used to measure the concentration of intra-regional 
CURRENT CONTEXT I DEMOGRAPHICS & GROWTH FORECAST 11

Table 5 Regional Employment by Industry Sectors, 2007-2040

Source: CA EDD, SCAG

Jobs by 2 Digit NAICS Sector

2007 2010 2015 2040

Difference 

(2010-2015)

Difference 

(2015-2040)

2013 Wage Number Level ($) (1,000) % Number 

(1,000) % Number (1,000) % Number (1,000) %

Total Employment (in Thousands) 8,002 100% 7,257 100% 8,006 100% 9,872 100%

749 (Annual 

Average % 

Change: 2.1%)

1,866 (Annual 

Average % 

Change: 0.9%)

52,126

Total Farm 69 0.9% 62 0.9% 65 0.8% 57 0.6% 0.0% -0.2% 27,811

Natural Resources and Mining 8 0.1% 7 0.1% 7 0.1% 5 0.1% 0.0% 0.0% 126,750

Utilities 49 0.6% 47 0.6% 48 0.6% 45 0.5% 0.0% -0.1% 99,700

Construction 470 5.9% 302 4.2% 369 4.6% 582 5.9% 0.5% 1.3% 55,585

Manufacturing 810 10.1% 658 9.1% 673 8.4% 638 6.5% -0.7% -1.9% 62,157

Wholesale Trade 410 5.1% 364 5.0% 397 5.0% 483 4.9% -0.1% -0.1% 61,782

Retail Trade 874 10.9% 775 10.7% 846 10.6% 967 9.8% -0.1% -0.7% 31,405

Transportation and Warehousing 316 3.9% 300 4.1% 326 4.1% 379 3.8% -0.1% -0.2% 51,539

Information 278 3.5% 254 3.5% 269 3.4% 308 3.1% -0.1% -0.2% 93,022

Finance and Insurance 322 4.0% 265 3.7% 284 3.5% 320 3.2% -0.1% -0.3% 95,719

Real Estate and Rental and Leasing 172 2.1% 154 2.1% 166 2.1% 204 2.1% -0.1% 0.0% 57,418

Professional, Scientific, and Technical 

Services 544 6.8% 477 6.6% 598 7.5% 864 8.8% 0.9% 1.3% 83,006

Management of Companies and 

Enterprises 103 1.3% 87 1.2% 96 1.2% 107 1.1% 0.0% -0.1% 94,986

Administrative and Support and Waste 

Services 622 7.8% 532 7.3% 598 7.5% 712 7.2% 0.1% -0.2% 35,515

Educational Services 692 8.6% 688 9.5% 715 8.9% 867 8.8% -0.6% -0.1% 49,719

Health Care and Social Assistance 910 11.4% 891 12.3% 1,021 12.8% 1,512 15.4% 0.5% 2.6% 43,678

Arts, Entertainment, and Recreation 159 2.0% 134 1.8% 152 1.9% 194 2.0% 0.1% 0.1% 63,060

Accommodation and Food Service 635 7.9% 690 9.5% 766 9.6% 875 8.9% 0.1% -0.7% 19,784

Other Services 314 3.9% 304 4.2% 340 4.2% 419 4.3% 0.1% 0.0% 33,415

Public Administration 246 3.1% 267 3.7% 270 3.4% 308 3.1% -0.3% -0.3% 74,118

Entropy Index (Normalized) 0.914 0.910 0.909 0.899
 12 2016–2040 RTP/SCS I APPENDIX

REGIONAL GROWTH FORECAST 

REGIONAL GROWTH FORECAST

SCAG projects that the region will add 3.8 million residents, 1.5 million households and 2.4 

million jobs over the RTP/SCS planning horizon (2012-2040) (see FIGURE 15 and FIGURE 

16). Population and households are projected to grow at the annual average growth rate of 

0.7 percent during the same period, while employment grows faster at two percent until 

2020, and then stabilizes at 0.7 percent. (see FIGURE 17). The SCAG region’s population 

is projected to grow slower than that of the previous years. The slow growth pattern is not 

present only in the SCAG region, but is also observed from U.S. and California population 

projections by U.S. Census Bureau and California DOF, respectively (see TABLE 2).

POPULATION

The slow population growth pattern experienced in the post-recession period is expected 

to continue into the future. Between 2015 and 2040, the annual population growth rate will 

be only 0.7 percent, which is similar to the post-recession period, but much lower than that 

experienced between 2000-2010. The region will grow mainly through natural increase (see 

TABLE 3 and FIGURE 19 and FIGURE 21). Nearly 90 percent of the population growth will be 

due to natural increase (e.g., births minus deaths) in the region rather than net migration (e.g., 

inmigration minus outmigration) (see FIGURE 20). The average number of babies per woman 

18.3 

7.4 

5.9 

18.8 

8.0 

5.9 

22.1 

9.9 

7.4 

Population Employment Households

2012 2015 2040

Figure 15 Population, Employment, and Households, SCAG Region, 2012, 2015 and 2040 

(in Millions)

Source: CA DOF, CA EDD, SCAG

Population Employment Households

0

5

10

15

20

25

2000 2020 2040

In Millions

Figure 16 Population, Employment, and Household Growth, SCAG Region, 2000-2040 (in Millions)

Source: CA DOF, CA EDD, SCAG

-8%

-6%

-4%

-2%

0

2%

4%

Population Employment

2000-2001 2014-2015 2039-2040

Annual Percent Change

Figure 17 Annual Percent Change of Population and Employment, SCAG Region, 2000-2040

Source: CA DOF, CA EDD, SCAG
CURRENT CONTEXT I DEMOGRAPHICS & GROWTH FORECAST 13

5% 3% 1% 1% 3% 5%

 0- 4

 5- 9

10-14

15-19

20-24

25-29

30-34

35-39

40-44

45-49

50-54

55-59

60-64

65-69

70-74

75-79

80-84

85+

2015 (Shaded) 2040

Males Females

Figure 18 Population Pyramids, SCAG Region, 2015 and 2040

Source: SCAG

Figure 19 Components of Population Change, SCAG Region, 2000-2040 (in Thousands)

Source: CA DOF, SCAG

0

50

100

150

200

250

300

2000-2001 2014-2015 2039-2040

Births Deaths

In Thousands

Figure 20 Births and Deaths, SCAG Region, 2000-2040 (in Thousands)

Source: CA DOF, SCAG

Natural Increase Net Migration

-100

-50

0

50

100

150

200

2000-2001 2014-2015 2039-2040

In Thousands

Figure 21 Net Immigration and Net Domestic Migration, SCAG Region, 2000-2040 (in Thousands)

Source: CA DOF, SCAG

Net Immigration Net Domestic Migration

-200

-150

-100

-50

0

50

100

150

2000-2001 2014-2015 2039-2040

In Thousands
 14 2016–2040 RTP/SCS I APPENDIX

HOUSEHOLDS

As the population ages and remains diverse in the region during the projection period, the 

householders are also aging and showing the change in the racial/ethnic distribution (see 

TABLE 4). Given the cohort size of the baby boomer generation, the effect of aging population 

on the number of households is enormous. The number of households will reach more 

than 7.4 million in 2040 with the net addition of over 1.4 million households in the next 25 

years. Older householders (65 years and older) will account for 75 percent of the projected 

household growth in the region and will increase their share from 21.6 percent in 2015 to 

31.6 percent in 2040 by ten percent. However, the share of householders 15-64 years old 

will decline from 78.4 percent to 68.4 percent. In particular, householders 15-24 years old 

will show the smallest increase among all age groups.

Following the changing dynamics of population projections, the region’s householders also 

experience a shift in the racial/ethnic composition during the projection period (see TABLE 

4). Hispanic householders will be the largest ethnic group in the region in 2040, and will 

continue to show the most growth among four racial/ethnic groups during the projection 

period. Hispanic householders will increase their share of total householders by 8.9 percent 

from 35.5 percent in 2015 to 44.4 percent in 2040. NH Asian/Other householders will have 

the highest growth mainly through immigration. The share of NH Asian/Other householders 

increases by 5.2 percent from 15.1 percent in 2015 to 20.1 percent in 2040. However, NH 

White householders will experience a net decline of 335,000 from 2.5 million in 2015 to 2.1 

million in 2040. The share of NH White householders will go down by 13 percent points from 

41.9 percent in 2015 to 28.9 percent in 2040. NH Black householders will also experience 

the smaller share of household growth (7.5 percent in 2015 vs. 6.6 percent in 2040). In 

contrast to the normalized entropy index for population, the normalized entropy index for 

householders will slightly increase from 0.875 in 2015 to 0.879 in 2040. 

There was an increase in the average household size from 3.0 in 2010 to 3.1 in 2015, but 

the household size will eventually decline from 3.1 in 2015 to 3.0 in 2040 as a result of the 

increase in the older householders and the increased headship rates of Hispanic and Asian/

Other populations. A smaller household size of both Hispanic and Asian/Other populations 

were made possible with an assumption that Hispanic and Asian/Other immigrants will have 

higher headship rates as they live in the U.S. for a longer period of time.

EMPLOYMENT

With an increase in jobs in the post-recession period (2010-15), the SCAG region’s economy 

returned to 2007 levels with an unemployment rate of 6.6 percent in 2015. The region is 

expected to add 1.9 million jobs, from 8 million in 2015 to 9.9 million in 2040. 

of child bearing age remains the same at 1.9 in both 2015 and 2040. The life expectancy of 

people in the region is expected to increase. International migration also plays an important 

role in population growth. Seven of ten new residents will be arriving in the region through 

international migration. 2.2 million more persons leave the region for the rest of the nation 

than persons migrating to the region between 2015-2040.

The most noticeable demographic characteristics of the projected population in the region 

will be the aging of the population and shifts in the racial/ethnic distribution (see TABLE 3 and 

FIGURE 18). First, the region’s median age is 35.4 in 2015 which is younger than the nation’s 

median age of 36.8. The region’s population is aging due to the aging of the baby boomer 

generation (born between 1946 and 1964) and the lower birth rate. The median age of the 

population is projected to increase by 2.5 years to 37.9 in 2040. The share of the population 

65 years old and over is projected to increase from 12 percent in 2015 to 18 percent in 2040, 

while the share of the population of 64 years old or less decreases from 88 percent in 2010 

to 82 percent in 2035. In particular, both children 15 years old or less and the working 

age population of 16-64 years old have shown a decline from 21 percent to 19 percent, 

and from 66 percent to 63 percent, respectively, during the projection period. The decline 

of the working age population may result in a potential shortage of workers and slower 

job growth unless the older population extends their retirement age. With the increasing 

share of the older population and the decreasing share of the working age population, the 

old-age dependency ratio is projected to increase from 19 percent in 2015 to 28 percent 

in 2040 by 9 percent. The older population will grow over six and half times faster than 

that of working age groups (16-64) during the same period. The older population, mainly 

composed of the baby boomer generation, will constitute 51 percent of the population growth 

between 2015 and 2040.

The region’s already high racial/ethnic diversity changes over time during the projection 

horizon (see TABLE 3). The Hispanic population will become the majority ethnic group in 

the region around 2027 and will continue to show the greatest growth due to births and 

immigration. The Hispanic population will increase its share of the population by 6.4 percent 

from 46.7 percent in 2015 to 53.1 percent in 2040. NH Asian/Other population, which 

includes the multiracial groups, will have the fastest growth mainly through immigration. 

The share of NH Asian/Other population increases from 15.6 percent in 2015 to 19.1 percent 

in 2040 by 3.5 percent. However, the NH White population will experience a net decline of 

940,000 from 5.9 million in 2015 to 5 million in 2040. The share of NH White population 

will decrease from 31.4 percent in 2015 to 22.4 percent in 2040 by 9 percent. NH Black 

population will also experience the smaller share of population growth (6.3 percent in 

2015 vs. 5.4 percent in 2040). As a result of the changing racial/ethnic composition, the 

normalized entropy index will decline from 0.853 in 2015 to 0.826 in 2040. 
CURRENT CONTEXT I DEMOGRAPHICS & GROWTH FORECAST 15

With a transformation of the region’s industrial structure, the economic quality of life of the 

region’s residents is severely affected. The distribution of jobs by wage level indicates that 

the region will increase the share of jobs in the lower 25 percent category, while there is a 

decrease in the share of jobs in the other job categories (note: the wage level is categorized 

into four levels: (1) bottom 25 percent, (2) lower 25 percent, (3) upper 25 percent, (4) 

top 25 percent). The jobs in the lower 25 percent category include (1) construction, (2) 

transportation and warehousing, (3) real estate and rental and leasing, (4) educational 

services, and (5) health care and social assistance. The share of the jobs in the bottom 

50 percent category increases from 65.2 percent in 2015 to 67.1 percent in 2040, while 

the share of the jobs in the top 50 percent category decreases from 34.8 percent to 33.0 

percent in 2040. The economic and job creation analysis appendix documents an analysis 

of the economic impacts of the 2016 RTP/SCS. 

SUBURBAN GROWTH

The region continued slow population growth in the post-recession period, adding only 

3.3 million people, while having fast jobs growth, adding nearly 1.9 million jobs from 

2015-2040. The HIOC, a measure of concentration, shows a decline from 58.19 in 

2015 to 55.00 in 2040 for population, and from 63.43 in 2015 to 59.53 in 2040 for 

employment (see TABLE 8). 

The declining HIOC indicates that there will be a deconcentration trend toward more growth 

of population and employment in Riverside and San Bernardino Counties. The share of both 

Riverside and San Bernardino Counties’ population increased from 23.5 percent in 2015 to 

26.6 percent in 2040 by 3.1 percent, while the share of both Riverside and San Bernardino 

Counties’ employment increased from 18.4 percent in 2015 to 22.2 percent in 2040 by 3.8 

percent. The fast growth of population and the faster growth of employment in these two 

counties made a major contribution to the downward change in the HIOC of the region from 

2015-2040. The gap of HIOC between population and employment becomes smaller in 

2040 than in 2015. The IOD decreased from 0.052 in 2015 to 0.045 in 2040. 

The region’s industrial mix, however, will experience continuous change over time due to 

the region’s relative competitiveness and globalization (see TABLE 5). The region’s relative 

competitiveness comes from the diversity of jobs in the region. The normalized entropy index 

for measuring the region’s job diversity is 0.909 in 2015. The region’s few jobs are relatively 

competitive compared to the national level. The location quotient (LQ) is used to measure 

the relative competitiveness (see TABLE 6). The region includes competitive high wage jobs 

such as (1) information, (2) manufacturing, (3) professional, scientific, and technical services, 

(4) wholesale trade and (5) arts, entertainment and recreation. The region also shows a 

strong competitiveness in low wage jobs including (1) accommodation and food service, (2) 

administrative and support services and (3) transportation and warehousing. 

The employment in the manufacturing sector is losing ground in the region as well as 

in the U.S. Globalization plays an important role in transforming the industrial structure 

of the region. It is clear that the region’s industrial structure evolves from productionoriented industries to service-oriented industries. For example, the share of employment 

in the manufacturing sector will continue to decrease from 8.6 percent in 2015 to 

6.4 percent in 2040.

A few selected sectors are expected to have rapid growth. The construction sector will 

regain its normal share by increasing from 4.6 percent in 2015 to 5.9 percent in 2040. This 

growth translates into 213,000 jobs from 2015-2040. The following four industrial sectors: 

(1) health care and social assistance (+491,000 jobs), (2) professional and business services 

(+266,000 jobs), (3) construction (+213,000), and (4) education services (+152,000) 

are key industrial sectors that are projected to add more than 1.1 million jobs by 2040 and 

account for 61 percent of total job growth from 2015-2040. The four industrial sectors 

increase their share from 34 percent in 2015 to 39 percent in 2040. While many service 

jobs require minimal skills and pay low wages, service jobs also include high-paying, highskill work, such as investment banking and computer operations. This changing composition 

of industrial sectors requires diverse skill needs for our industries.

Table 6 Regional Employment by Wage Level, 2015 and 2040

Note: 1 = (1) Natural Resources and Mining, (2) Utilities, (3) Information, (4) Finance and Insurance, (5) Management of Companies and Enterprises; 2= (1) Manufacturing, (2) Wholesale Trade, (3) Professional, Scientific and Technical Services, (4) Arts, 

Entertainment, and Recreation, (5) Public Administration; 3= (1) Construction, (2) Transportation and Warehousing, (3) Real Estate and Rental and Leasing, (4) Educational Services, (5) Health Care and Social Assistance; 4= (1) Total Farm, (2) Retail Trade, (3) 

Administrative and Support and Waste Services, (4) Accommodation and Food Service, (5) Other Services.

2013 Wage Level 2007 2010 2015 2040 Difference 

(2010-2015)

Difference 

(2015-2040)

1 - top 25% 9.5% 9.1% 8.8% 7.9% -0.3% -0.9%

2 - upper 25% 27.1% 26.2% 26.0% 25.1% -0.2% -0.9%

3 - lower 25% 32.0% 32.2% 32.5% 36.2% 0.3% 3.7%

4 - bottom 25% 31.4% 32.6% 32.7% 30.9% 0.2% -1.8%
 16 2016–2040 RTP/SCS I APPENDIX

FORECAST METHODOLOGY AND ASSUMPTIONS

GROWTH FORECAST APPROACH

SCAG’s Regional Growth Forecast includes three major indicators: population, households 

and employment. As past practice, SCAG uses the BULA (Balance, Uncertainty, Latest, and 

Adaptive) and Collaborative approach toward developing the regional growth forecast for 

2016 RTP/SCS. SCAG’s growth forecast process has been open, transparent, and extensive. 

Such an inclusive process involves broad participation from experts and stakeholders 

All counties in the region experienced a decline in P-E ratio from 2015-2040. The regional 

P-E ratio declined from 2.3 to 2.2. Riverside and San Bernardino Counties experienced a 

faster decline in the P-E ratio than other counties: 3.1 in 2015 to 2.7 in 2040 for Riverside 

County; 2.9 in 2015 to 2.7 in 2040 for San Bernardino County. 

If the region continues to experience faster employment growth in Riverside and San 

Bernardino Counties, where an abundant labor force is available, the region’s serious 

transportation and air quality problems may be reduced due to more balanced county 

distribution of population and employment.

Jobs by 2 digit NAICS sector 2007 2010 2015 2040 Difference 

(2010-2015)

Difference 

(2015-2040)

Total Farm 0.635 0.565 0.591 0.591 0.026 0.000

Natural Resources and Mining 0.210 0.198 0.182 0.127 -0.016 -0.055

Utilities 1.130 1.111 1.139 1.173 0.028 0.034

Construction 0.925 0.813 0.855 1.064 0.042 0.210

Manufacturing 1.068 1.084 1.100 1.150 0.016 0.050

Wholesale Trade 1.237 1.263 1.256 1.312 -0.007 0.056

Retail Trade 0.996 0.992 0.996 0.992 0.004 -0.004

Transportation and Warehousing 0.992 1.069 1.056 1.035 -0.013 -0.021

Information 1.646 1.737 1.737 1.869 0.000 0.132

Finance and Insurance 0.933 0.871 0.862 0.840 -0.009 -0.022

Real Estate and Rental and Leasing 1.213 1.282 1.262 1.327 -0.020 0.065

Professional, Scientific and Technical Services 1.156 1.093 1.129 1.169 0.036 0.040

Management of Companies and Enterprises 1.032 0.915 0.951 0.922 0.036 -0.029

Administrative and Support and Waste Services 1.256 1.248 1.241 1.142 -0.007 -0.099

Educational Services 0.965 0.976 0.927 0.881 -0.049 -0.046

Health Care and Social Assistance 0.986 0.948 0.915 0.887 -0.033 -0.028

Arts, Entertainment, and Recreation 1.253 1.139 1.165 1.169 0.027 0.004

Accommodation and Food Service 1.013 1.186 1.229 1.172 0.043 -0.057

Other Services 0.801 0.837 0.854 0.881 0.017 0.026

Public Administration 0.480 0.526 0.509 0.514 -0.017 0.005

Source: U.S. BLS, CA EDD, SCAG

Table 7 Regional Location Quotients for Industry Sectors, 2007-2040
CURRENT CONTEXT I DEMOGRAPHICS & GROWTH FORECAST 17

LOCAL INPUT

The initial mid-range regional growth forecast was further disaggregated into the small 

area level. SCAG provided local jurisdictions with the SCAG’s multi-level small area growth 

forecast for their review and comments. SCAG’s staff conducted one-on-one meetings with 

195 of 197 jurisdictions to review the forecast and to receive local input. This local input 

process provided an opportunity for jurisdictions to offer their local knowledge and input to 

inform SCAG’s regional datasets. After SCAG received input from local jurisdictions, SCAG 

assessed the reasonableness of the aggregated local input data at the regional level by 

using the unemployment rate, and SCAG also evaluated the comments and incorporated 

the adjustments into the population, household and employment growth distributions. The 

resulting final local input growth forecast serves as a basis for further developing the policy 

growth forecast. Additional refinements to the final local input growth forecast were made 

to reflect land use-transportation coordination through the scenario planning process in the 

development of the policy growth forecast (see EXHIBITS 1-9).

GROWTH FORECAST METHODOLOGY

The regional growth forecast for the 2016-2040 RTP/SCS was developed using the 

regional forecast methodology used in the development of the 2012 RTP growth forecast 

and updated demographic-economic assumptions (see SCAG’s growth forecast report 

for 2012 RTP/SCS: http://rtpscs.scag.ca.gov/Documents/2012/final/SR/2012fRTP_

GrowthForecast.pdf). The following is the methodology for developing the regional growth 

forecast for the 2016 RTP/SCS. 

SCAG projects regional employment using a shift-share model. The shift-share model 

computes employment comprised of 20 broad NAICS sectors, at a future point in time using 

a regional share of the nation’s employment. The regional employment forecasts are based 

on a set of national employment forecasts. The national employment forecasts have two 

components: 1) forecasts of the number of total jobs and 2) forecasts of jobs by industry 

sector. The regional job projections depend both on the number of total jobs in the U.S. and 

the distribution of these jobs among industry sectors.

The forecast of total U.S. jobs is based on a forecast of total population, population by 

age group, labor force participation rates, assumed unemployment, and the ratio of jobs 

to workers (employed residents) reflecting assumptions about multiple job holding for 

individuals. The population by age group and labor force participation rate forecasts 

are quantitatively more important than the other assumptions in developing national 

projections of total jobs.

specifically. The following three major activities (panel of experts meeting, range of regional 

growth forecasts, and local input) were essential in developing the regional growth forecast 

with demographic-economic assumptions.

PANEL OF EXPERTS MEETING (2013) 

The collective expert opinions are a useful reference to reduce the short-term and longterm projection errors. SCAG held the 2013 SCAG panel of demographic and economic 

experts meeting on June 27, 2013 to review SCAG’s methodology and assumptions for its 

population, household, and employment growth forecast for the 2016-2040 RTP/SCS. 

Twenty (20) academic scholars and leading practitioners were invited to participate on the 

panel. The panel of experts reviewed demographic and economic trends in the national and 

regional growth context, discussed the key assumptions underlying the regional and county 

growth forecast, and provided responses to survey questions on major assumptions. 

A RANGE OF REGIONAL GROWTH FORECASTS

SCAG initially sets a range of regional growth forecasts (population, employment, and 

households) to address the uncertainty of a certain set of growth forecasts. A set of regional 

growth forecasts are developed in the following order: employment, population and 

households (Field and MacGregor, 1987). The regional employment forecast is initially 

developed and followed by the population forecast, and then by the household forecast. 

First, a range of the regional employment forecasts (low, mid, high) is derived using a range 

of the regional shares of the national jobs as suggested by the expert panel. Second, a 

range of regional employment forecasts is translated into a range of the regional population 

forecasts (low, mid, high) using a set of demographic assumptions. All related economic 

and demographic assumptions (e.g., unemployment rates, labor force participation rates, 

immigration level, fertility rates, and survival rates, etc.) remain unchanged for three different 

employment levels. Third, a range of the regional population forecasts are translated into a 

range of the regional household forecasts using a mid-trend method to convert population 

into households. It is based on the trend extrapolation of headship rates by age, sex, and 

race/ethnicity with a consideration of the assimilation assumptions of the Hispanic and 

Asian headship rates. 
 18 2016–2040 RTP/SCS I APPENDIX

Table 8 Regional Population and Employment by County, 2000-2040

2000 2010 2015 2040 Difference

(2010-2015)

Difference

(2015-2040)

County/Region Number 

(1,000) % Number (1,000) % Number (1,000) % Number (1,000) % Number (1,000) % Number (1,000) %

POPULATION

Imperial 143 0.9% 176 1.0% 182 1.0% 282 1.3% 7 0.0% 100 0.3%

Los Angeles 9,544 57.6% 9,827 54.4% 10,159 54.1% 11,514 52.0% 332 -0.3% 1,355 -2.0%

Orange 2,854 17.2% 3,017 16.7% 3,157 16.8% 3,461 15.6% 140 0.1% 304 -1.2%

Riverside 1,557 9.4% 2,192 12.1% 2,316 12.3% 3,183 14.4% 125 0.2% 867 2.1%

San Bernardino 1,719 10.4% 2,039 11.3% 2,111 11.2% 2,731 12.3% 72 0.0% 620 1.1%

Ventura 757 4.6% 825 4.6% 853 4.5% 966 4.4% 28 0.0% 113 -0.2%

SCAG Region 16,574 100.0% 18,078 100.0% 18,779 100.0% 22,138 100.0% 703 3,359

HIOC* 62.09 58.34 58.19 55.00 -0.1 -3.2

EMPLOYMENT

Imperial 54 0.7% 56 0.8% 76 0.9% 125 1.3% 20 0.2% 49 0.4%

Los Angeles 4,445 59.7% 4,140 57.1% 4,463 55.7% 5,226 52.9% 323 -1.3% 763 -2.4%

Orange 1,517 20.4% 1,493 20.6% 1,633 20.4% 1,899 19.2% 140 -0.2% 266 -1.1%

Riverside 514 6.9% 592 8.2% 742 9.3% 1,175 11.9% 150 1.1% 433 2.5%

San Bernardino 587 7.9% 653 9.0% 729 9.1% 1,028 10.4% 76 0.1% 299 1.3%

Ventura 323 4.3% 323 4.4% 363 4.5% 420 4.3% 40 0.1% 57 -0.2%

SCAG Region 7,440 100.0% 7,257 100.0% 8,006 100.0% 9,872 100.0% 749 1,866

HIOC* 67.41 64.91 63.43 59.53 -1.48 -3.9

IOD** 0.054 0.066 0.052 0.045 -0.013 -0.007

P-E RATIO

Imperial 2.6 3.1 2.4 2.3 -0.7 -0.1

Los Angeles 2.1 2.4 2.3 2.2 -0.1 -0.1

Orange 1.9 2.0 1.9 1.8 -0.1 -0.1

Riverside 3.0 3.7 3.1 2.7 -0.6 -0.4

San Bernardino 2.9 3.1 2.9 2.7 -0.2 -0.2

Ventura 2.3 2.6 2.4 2.3 -0.2 -0.1

SCAG Region 2.2 2.5 2.3 2.2 -0.1 -0.1

Note: * HIOC (Hoover Index of Concentration) measures the distribution of population and employment. If HIOC equals 0, then population and employment are perfectly de-concentrated. If HIOC equals 100, then the county’s share in comparison with the entire 

SCAG region’s population or employment would be concentrated to a single county of the SCAG region. However, if the HIOC drops to 0, then each county’s share would be equal. **IOD (Index of Divergence) measures the intra-regional segregation of population 

and employment. If ID equals 1, then the population and employment of a county are unbalanced. If IOD equals 0, then the population and the employment of a county are spatially proportioned. 

Source: CA DOF, CA EDD, SCAG
CURRENT CONTEXT I DEMOGRAPHICS & GROWTH FORECAST 19

SCAG projects regional population using the cohort-component model. The model computes 

population at a future point in time by adding to the existing population the number of group 

quarters population, births and persons moving into the region during a projection period, 

and by subtracting the number of deaths and the number of persons moving out of the 

region. The patterns of migration into and out of the region are determined by the number 

of forecasted jobs. 

Households are forecasts by multiplying the projected residential population by projected 

headship rates. The headship rate is the proportion of a population cohort that forms the 

household. Age-sex-racial/ethnic specific household formation levels are used to translate 

projected residential population into projected households. 

The regional growth forecast is further disaggregated to the county and the smaller 

geographies. The preliminary county level growth forecast was derived using the county 

share of the regional growth forecast from 2012 RTP/SCS county growth forecast, and was 

later refined as a result of local input.

REGIONAL DEMOGRAPHIC-ECONOMIC ASSUMPTIONS 

Demographic and economic assumptions play a decisive role in determining the size of 

population, households, and employment in the future (see TABLE 9). Population size is 

projected by identifying the demographic rates (e.g., fertility rate, survival rate, migration 

rate) of the population cohort. The region’s total fertility rate remains at 1.9, which is lower 

than the replacement level of 2.1, during the projection period. The region’s life expectancy 

at birth improves at the same rate as that of the nation’s life expectancy improvement as 

assumed by the U.S. Census Bureau’s 2014 population projection. Domestic migration 

fluctuates and is directly influenced by labor demand derived from regional employment 

forecasts. International net immigration will be 63,000 per year until it increases to 96,000 

per year in 2020. The share of Hispanic and Asian migrants in the nation will increase along 

with the increasing Hispanic and Asian population size. 

In addition to demographic assumptions, three translation factors are needed to link regional 

employment forecasts to regional population forecasts. They are labor force participation 

rates, the implied unemployment rates and multiple jobholding rates. First, labor force 

participation rates play an important role in translating the labor force demand into labor 

force supply. The projected pattern of national labor force participation rates developed by 

Pitkin and Myers in 2013 was used to project SCAG region’s labor force participation rates. 

The overall labor force participation rate is projected to decline from 64.5 percent in 2010 

to 62.2 percent in 2040. Second, some workers may keep two or more jobs. The double 

jobholding rate will be 4.5 percent of the workers during the projection period. Third, the 

implied unemployment rate will range from five percent to eight percent during the projection 

period. The implied unemployment rate is derived by matching labor supply estimated 

Table 9 Regional Demographic-Economic Assumptions

Note: NH - Non-Hispanic

Source: CA DOF, SCAG

Race/Ethnicity 2010-2015 

(Annual Average)

2015-2040 

(Annual Average)

Difference 

(2015-2040)

TOTAL FERTILITY RATE

White (NH) 1.5 1.5 0

Black (NH) 1.7 1.7 0

Asian & Others (NH) 1.6 1.6 0

Hispanic 2.1 2.1 0

Total 1.9 1.9 0 CRUDE DEATH RATE

White (NH) 11.4 11.9 0.5

Black (NH) 9.1 9.4 0.3

Asian & Others (NH) 2.6 4.0 1.4

Hispanic 2.8 3.5 0.7

Total 6.2 6,4 0.2 INTERNATIONAL NET IMMIGRATION

Total 62,941 95,950 33,000

White (NH) 11% 11% 0%

Black (NH) 3% 3% 0%

Asian & Others (NH) 19% 19% 0%

Hispanic 68% 68% 0%

Total 100% 100% 0% LABOR FORCE PARTICIPATION RATE 

White (NH) 63.5% 60.7% -2.8%

Black (NH) 59.6% 56.7% -2.9%

Asian & Others (NH) 63.3% 59.8% -3.5%

Hispanic 66.4% 64.4% -3.0%

Total 64.5% 62.2% -2.3%
 20 2016–2040 RTP/SCS I APPENDIX

from population projections with workers estimated from job projections. Finally, the most 

important consideration is the reasonable regional share of national jobs. The SCAG region’s 

share of the national jobs in 2040 is assumed to remain at the 5.3 percent observed in 2015. 

TABLE 10 shows the projected headship rates by age, sex and race/ethnicity between 

2015 and 2040, which are the basis for deriving the household forecast. The headship 

rate projections were developed using the trend extrapolation of headship rates with an 

assumption of the assimilation of Hispanic and NH Asian/Other headship rates. The overall 

headship rates will increase from 40.3 percent in 2015 to 41.3 percent in 2040 (see TABLE 

10). As a result of the assimilation of Hispanic and NH Asian/Other headship rates, Hispanic 

headship rates increase from 33.1 percent in 2015 to 36.2 percent in 2040 by 3.1 percent, 

and NH Asian/Other headship rates increase from 38.3 percent in 2015 to 42.7 percent in 

2040 by 4.5 percent. The female headship rates also increase due to the higher labor force 

participation, marriage postponement, and longer life expectancy.

Note: *2015 headship rates were derived using 2010 Census and 2014 Annual ACS data. **The 2040 Asian and Hispanic headship rates reflect an assumption of assimilation. A headship rate assumption with assimilation is developed in the following way, 

Asian headship rates are reduced by 50 percent of the difference from 2010 White headship rates by 2050; Hispanic headship rates are reduced by 25 percent of the difference from 2010 White headship rates by 2050. NH - Non-Hispanic. Source: U.S.Census 

Bureau, CA DOF, SCAG

2000 2010 2015 2040 Difference (2010-2015) Difference (2015-2040)

AGE

15-24 9.9% 7.1% 6.4% 6.3% -0.4% -0.1%

25-34 40.1% 36.2% 34.3% 33.2% -1.0% -1.1%

35-44 50.4% 48.8% 47.5% 46.9% -1.4% -0.6%

45-54 54.6% 52.8% 51.2% 50.3% -1.7% -0.9%

55-64 56.3% 54.3% 52.3% 50.9% -1.8% -1.4%

65-74 58.5% 56.2% 55.3% 53.6% -1.9% -1.7%

75+ 60.4% 60.9% 57.8% 55.1% -2.2% -2.7%

SEX

Male 58.3% 45.6% 42.7% 42.9% -1.1% 0.2%

Female 28.5% 37.2% 37.9% 39.7% -0.7% 1.8%

RACE/

ETHNICITY

White (NH) 51.2% 50.5% 49.4% 49.7% -0.9% 0.3%

Black (NH) 49.1% 48.6% 47.2% 48.4% -0.4% 1.2%

Asian & Others (NH) 38.7% 38.4% 38.5% 42.5% -0.1% 4.0%

Hispanic 34.2% 33.3% 32.7% 36.0% -0.2% 3.3%

Total 43.1% 41.3% 40.3% 41.3% -0.8% 1.0%

Table 10 Regional Headship Rates by Age, Sex, and Race/Ethnicity, 2000-2040
CURRENT CONTEXT I DEMOGRAPHICS & GROWTH FORECAST 21

SMALL AREA FORECAST AND ALLOCATION

A critical input driving SCAG’s planning process is the Regional Growth Forecast. The 

Regional Growth Forecast at the jurisdictional and TAZ levels are the basis for developing 

the Regional Transportation Plan (RTP), Sustainable Communities Strategy (SCS), Program 

Environmental Impact Report (PEIR), and the Regional Housing Needs Assessment (RHNA). 

SCAG’s 2016 RTP/SCS growth forecast includes six counties’ jurisdictional level population, 

household, and employment for years 2012, 2020, 2035 and 2040. 

JURISDICTIONAL GROWTH FORECASTING

Based on the growth forecast at the regional level described in the previous charters, SCAG 

further projects jurisdictional level population, household and employment. The latest 

jurisdictions’ existing and general plan land use serve as the basis for future year population 

and household allocations. Household growth rates and household size are estimated 

based on historical trends and the developable capacity from the local jurisdiction’s general 

plan. Population projections are calculated based on household growth and household 

size. Future jurisdictional employment is estimated based on the share of the county’s 

employment by sector. It is further adjusted to account for population serving jobs, such as 

Retail and Service, which are highly correlated with population growth. 

The following major data sources are considered and used in the development of 

the growth forecast: 

z California Department of Finance (DOF) population and household estimates;

z California Employment Development Department (EDD) jobs report by industry;

z Regional Housing Needs Assessment (RHNA) growth projections for 

years 2014 through 2021; 

z 2012 existing land use and General Plans from local jurisdictions;

z 2010 Census and the latest American Community Survey (ACS) data; and

z 2011 Business Installment data from InfoGroup.

After the initial growth forecast was developed, SCAG’s staff conducted one-on-one 

meetings with 195 of 197 jurisdictions in the region to review the forecast and receive local 

input. This local input process provided an opportunity for jurisdictions to offer their local 

knowledge and input to inform SCAG’s regional datasets. SCAG evaluated the comments 

and incorporated the adjustments into the population, household, and employment growth 

distributions. These adjustments also include the incorporations of approved projects 

provided by the local jurisdictions. The resulting Draft 2016 RTP/SCS growth forecast 

served as the basis for the initial 2016 RTP/SCS evaluation.

Below are the guiding principles, which are the basis for developing the draft Policy 

Growth Forecast (PGF):

z Principle #1: The draft PGF for the 2016 RTP/SCS shall be adopted by the 

Regional Council at the jurisdictional level, thus directly reflecting the population, 

household and employment growth projections derived from the local input and 

previously reviewed and approved by SCAG’s local jurisdictions. The draft PGF 

maintains these projected jurisdictional growth totals, meaning future growth is not 

reallocated from one local jurisdiction to another.

z Principle #2: The draft PGF at the TAZ level is controlled to be within the density 

ranges* of local general plans or input received from local jurisdictions in this most 

recent round of review.

z Principle #3: For the purpose of determining consistency for California 

Environmental Quality Act (CEQA), lead agencies such as local jurisdictions 

have the sole discretion in determining a local project’s consistency 

with the 2016 RTP/SCS.

z Principle #4: Transportation Analysis Zone (TAZ) level data or any data at a 

geography smaller than the jurisdictional level is included in the draft PGF only 

to conduct the required modeling analysis and is therefore, only advisory and 

non-binding because SCAG’s sub-jurisdictional forecasts are not to be adopted as 

part of the 2016 RTP/SCS. After SCAG’s adoption of the PGF at the jurisdictional 

level, the TAZ level data may be used by a jurisdiction in local planning as it deems 

appropriate and there is no obligation by a jurisdiction to change its land use 

policies, General Plan, or regulations to be consistent with the RTP/SCS. SCAG 

staff plans to monitor the use of this data after the adoption of the RTP/SCS to 

encourage appropriate use.

z Principle #5: SCAG staff continues to communicate with other agencies who 

use SCAG subjurisdictional level data to ensure that the “advisory & nonbinding” nature of the dataset is appropriately maintained. (See Attachment 1 for 

information regarding SCAG’s communications with SCAQMD and ARB about the 

use of SCAG’s sub-jurisdictional level data).

Consistent with the above stated principles, the preferred scenario and corresponding 

forecast of population, household and employment growth is adopted at the jurisdictional 

level as part of the 2016 RTP/SCS, and sub-jurisdictional level data and/or maps associated 

with the 2016 RTP/SCS is advisory only. For purposes of qualifying for future funding 

opportunities and/or other incentive programs, sub-jurisdictional data and/or maps used to 

determine consistency with the Sustainable Communities Strategy shall only be used at the 

discretion and with the approval of the local jurisdiction. However, this does not otherwise 

limit the use of the sub-jurisdictional data and/or maps by SCAG, County Transportation 

Commissions, Councils of Governments, SCAG Subregions, Caltrans, and other public 

*With the exception of the 6 percent of TAZs that have average density below the density range of local general plans.
 22 2016–2040 RTP/SCS I APPENDIX

agencies for transportation modeling and planning purposes. Any other use of the subjurisdictional data and/or maps not specified herein, shall require agreement from the 

Regional Council, respective policy committees and local jurisdictions.

LOCAL INPUT PROCESS

Local input plays an important role in developing a reasonable growth forecast for the 

2016 RTP/SCS. SCAG’s Bottom-Up Local Input Process began in March 2013 and has 

been designed to engage local jurisdictions in establishing the base geographic and 

socioeconomic projections including population, household and employment.

z March 2013: Each jurisdiction was contacted individually and was requested to 

provide their base general plan, land use, and zoning data to SCAG.

z June 2013: With approval from SCAG’s Community Economic Human 

Development (CEHD) Committee, the protocol for local jurisdictions to 

provide input and approval of SCAG’s geographic and socioeconomic 

datasets was established.

z October 2013: Based on guidance from the CEHD, the Technical Working Group 

(TWG), and our subregional partners, SCAG staff distributed the schedule, 

protocol, and summary descriptions of SCAG’s base datasets in a letter to all 

regional city managers, planning directors, city clerks (for forwarding to all elected 

officials), subregional executive directors, and subregional coordinators.

z November 2013 through January 2014: With input from the CEHD, TWG, and 

subregional staff, SCAG staff rolled-out the draft growth forecast including 

Population, Household, and Employment.

z December 2013 through August 2014: With support from our subregional 

partners and oversight from the CEHD, staff met with 99 percent of SCAG’s 197 

jurisdictions one-on-one and received feedback from 93 percent of jurisdictions on 

all or a portion of our information requests.

z June 2015 through July 2015: SCAG distributed the draft policy growth forecast 

to the local jurisdictions again to seek additional feedback.

z During the following month, SCAG staff incorporated all the comments received in 

the draft policy growth forecast as part of the draft plan. 

The close collaboration enables us to form the growth projection which reflects the locals’ 

visions. The TABLE 11 presents the local input based jurisdictional level growth forecast. 
CURRENT CONTEXT I DEMOGRAPHICS & GROWTH FORECAST 23

County City Name 2012 

Population

 2040 

Population

 2012 

Household

 2040 

Household

2012 

Employment

 2040 

Employment

Imperial Brawley city 25,800 42,900 7,600 15,000 8,000 16,800

Imperial Calexico city 40,200 62,200 10,200 19,300 8,300 17,500

Imperial Calipatria city 7,600 9,600 1,000 1,600 1,300 2,200

Imperial El Centro city 44,100 61,000 13,100 19,900 20,300 43,800

Imperial Holtville city 6,100 8,000 1,800 2,500 1,000 2,000

Imperial Imperial city 15,800 25,400 4,600 8,800 3,400 9,500

Imperial Westmorland city 2,300 2,700 600 700 300 500

Imperial Unincorporated 37,700 70,300 10,400 24,700 16,400 32,300

Los Angeles Agoura Hills city 20,500 22,700 7,300 8,200 12,500 15,300

Los Angeles Alhambra city 84,000 88,800 29,300 31,900 28,000 33,500

Los Angeles Arcadia city 56,700 65,900 19,600 22,900 28,900 34,400

Los Angeles Artesia city 16,600 18,000 4,500 5,000 5,000 5,800

Los Angeles Avalon city 3,800 5,100 1,500 2,100 2,500 3,000

Los Angeles Azusa city 47,100 55,000 12,800 15,600 16,600 20,600

Los Angeles Baldwin Park city 76,100 83,600 17,200 19,300 16,500 19,500

Los Angeles Bell city 35,700 36,900 8,900 9,200 12,400 13,700

Los Angeles Bellflower city 77,100 79,600 23,700 24,400 13,600 14,700

Los Angeles Bell Gardens city 42,300 44,000 9,700 10,100 9,400 10,500

Los Angeles Beverly Hills city 34,400 37,200 14,900 16,200 57,700 68,900

Los Angeles Bradbury city 1,100 1,200 400 400 100 200

Los Angeles Burbank city 103,300 118,700 42,500 48,400 106,800 145,000

Los Angeles Calabasas city 23,800 24,500 8,700 9,100 16,700 17,300

Los Angeles Carson city 92,000 107,900 25,300 30,800 58,500 69,700

Los Angeles Cerritos city 49,300 50,900 15,500 16,000 30,400 33,700

Los Angeles Claremont city 35,500 39,400 11,700 13,200 17,400 19,700

Los Angeles Commerce city 12,900 13,500 3,400 3,600 44,600 49,100

Los Angeles Compton city 97,300 100,900 23,100 24,000 25,400 28,200

Los Angeles Covina city 48,200 51,600 15,900 17,200 25,300 29,500

Los Angeles Cudahy city 23,800 23,800 5,600 5,600 2,900 2,900

Los Angeles Culver City city 39,100 40,700 16,800 17,500 44,100 53,000

Los Angeles Diamond Bar city 56,000 63,900 17,900 21,200 15,400 19,300

Los Angeles Downey city 112,500 121,700 33,900 37,300 47,500 53,000

Table 11 Jurisdictional Forecast 2040
 24 2016–2040 RTP/SCS I APPENDIX

County City Name 2012 

Population

 2040 

Population

 2012 

Household

 2040 

Household

2012 

Employment

 2040 

Employment

Los Angeles Duarte city 21,500 24,300 7,000 8,200 10,100 11,900

Los Angeles El Monte city 114,200 137,200 27,800 34,700 28,000 35,700

Los Angeles El Segundo city 16,700 17,300 7,100 7,400 38,400 45,400

Los Angeles Gardena city 59,400 68,700 20,600 24,200 28,900 33,500

Los Angeles Glendale city 193,200 214,000 72,400 81,100 111,300 127,000

Los Angeles Glendora city 50,500 54,300 17,200 18,900 20,000 23,000

Los Angeles Hawaiian Gardens city 14,300 15,900 3,600 4,000 4,800 5,600

Los Angeles Hawthorne city 85,300 87,000 28,600 30,000 27,200 32,100

Los Angeles Hermosa Beach city 19,600 20,400 9,500 9,800 7,400 10,000

Los Angeles Hidden Hills city 1,900 2,000 600 600 300 300

Los Angeles Huntington Park city 58,500 67,400 14,600 17,400 15,600 18,600

Los Angeles Industry city 500 500 100 100 67,700 74,700

Los Angeles Inglewood city 110,900 129,000 36,600 43,300 31,100 37,400

Los Angeles Irwindale city 1,400 2,000 400 500 18,800 21,500

Los Angeles La Cañada Flintridge 

city 20,400 21,600 6,900 7,300 6,500 8,300

Los Angeles La Habra Heights city 5,400 6,200 1,800 1,900 200 400

Los Angeles Lakewood city 80,600 84,700 26,600 28,200 18,900 21,400

Los Angeles La Mirada city 48,800 52,100 14,700 15,800 17,400 20,200

Los Angeles Lancaster city 158,300 209,900 47,400 65,300 45,800 59,600

Los Angeles La Puente city 40,100 50,200 9,500 12,400 6,300 8,700

Los Angeles La Verne city 31,800 32,900 11,400 12,100 12,200 14,300

Los Angeles Lawndale city 33,000 33,900 9,700 10,100 6,700 8,200

Los Angeles Lomita city 20,500 21,200 8,100 8,400 4,600 5,400

Los Angeles Long Beach city 466,300 484,500 163,800 175,500 153,200 181,700

Los Angeles Los Angeles city 3,845,500 4,609,400 1,325,500 1,690,300 1,696,400 2,169,100

Los Angeles Lynwood city 70,300 76,100 14,700 16,200 9,200 10,900

Los Angeles Malibu city 12,700 14,100 5,300 5,600 8,500 10,300

Los Angeles Manhattan Beach city 35,300 37,100 14,000 14,800 18,000 20,700

Los Angeles Maywood city 27,500 28,900 6,600 6,900 3,600 4,000

Los Angeles Monrovia city 36,800 40,300 13,800 15,300 19,700 23,300

Los Angeles Montebello city 63,000 67,300 19,100 21,000 27,500 30,800

Table 11 Jurisdictional Forecast 2040 Continued
CURRENT CONTEXT I DEMOGRAPHICS & GROWTH FORECAST 25

County City Name 2012 

Population

 2040 

Population

 2012 

Household

 2040 

Household

2012 

Employment

 2040 

Employment

Los Angeles Monterey Park city 61,300 65,000 20,200 21,500 32,500 36,500

Los Angeles Norwalk city 105,900 106,300 27,100 27,200 24,100 27,300

Los Angeles Palmdale city 154,200 201,500 43,100 59,300 29,300 40,300

Los Angeles Palos Verdes Estates 

city 13,600 13,900 5,100 5,200 2,300 2,900

Los Angeles Paramount city 54,500 58,000 13,900 14,800 19,600 22,300

Los Angeles Pasadena city 140,300 150,700 58,900 62,400 111,000 144,800

Los Angeles Pico Rivera city 63,400 69,100 16,600 18,400 18,900 22,400

Los Angeles Pomona city 150,500 190,400 38,600 51,100 55,100 67,200

Los Angeles Rancho Palos Verdes 

city 42,000 42,300 15,600 15,700 5,800 6,200

Los Angeles Redondo Beach city 67,200 74,400 29,000 33,000 24,000 29,800

Los Angeles Rolling Hills city 1,900 2,000 700 700 100 100

Los Angeles Rolling Hills Estates 

city 8,100 8,600 3,000 3,100 5,900 6,800

Los Angeles Rosemead city 54,300 60,800 14,300 16,400 13,700 16,200

Los Angeles San Dimas city 33,600 34,500 12,000 12,400 11,200 12,700

Los Angeles San Fernando city 23,900 26,900 6,000 7,000 10,900 12,700

Los Angeles San Gabriel city 40,100 46,900 12,600 15,300 14,100 16,800

Los Angeles San Marino city 13,200 13,300 4,300 4,400 3,600 4,200

Los Angeles Santa Clarita city 202,000 262,200 67,300 90,300 73,500 95,900

Los Angeles Santa Fe Springs city 16,600 21,700 4,800 6,500 54,600 62,000

Los Angeles Santa Monica city 90,700 103,400 47,100 53,900 89,600 103,700

Los Angeles Sierra Madre city 11,000 11,200 4,800 5,000 1,900 2,100

Los Angeles Signal Hill city 11,200 12,000 4,200 4,600 13,800 16,500

Los Angeles South El Monte city 20,300 22,500 4,600 5,200 15,700 17,800

Los Angeles South Gate city 94,700 111,800 23,200 28,300 20,400 24,000

Los Angeles South Pasadena city 25,800 27,100 10,500 11,100 9,300 10,500

Los Angeles Temple City city 35,900 40,600 11,600 13,500 6,900 8,400

Los Angeles Torrance city 146,500 159,800 56,100 62,000 102,300 117,600

Los Angeles Vernon city 100 300 0 100 43,200 46,100

Los Angeles Walnut city 29,800 33,800 8,700 10,400 8,400 9,900

Los Angeles West Covina city 107,000 116,700 31,700 35,000 29,500 34,300

Table 11 Jurisdictional Forecast 2040 Continued
 26 2016–2040 RTP/SCS I APPENDIX

County City Name 2012 

Population

 2040 

Population

 2012 

Household

 2040 

Household

2012 

Employment

 2040 

Employment

Los Angeles West Hollywood city 34,800 41,800 22,600 27,800 29,800 37,300

Los Angeles Westlake Village city 8,300 8,800 3,300 3,500 13,300 15,900

Los Angeles Whittier city 85,900 96,900 28,300 32,600 26,900 31,700

Los Angeles Unincorporated 1,040,700 1,273,700 292,700 392,400 222,900 288,400

Orange Aliso Viejo city 49,300 51,000 18,500 19,400 18,900 20,900

Orange Anaheim city 345,300 403,400 99,200 122,600 177,900 245,600

Orange Brea city 41,100 50,600 14,500 18,100 46,700 53,700

Orange Buena Park city 81,800 92,500 24,000 27,900 34,300 39,800

Orange Costa Mesa city 111,200 116,400 40,000 42,500 84,400 93,200

Orange Cypress city 48,500 49,700 15,700 16,300 22,100 27,700

Orange Dana Point city 33,800 35,800 14,200 15,300 11,900 14,100

Orange Fountain Valley city 56,000 59,300 18,700 19,900 30,400 34,900

Orange Fullerton city 138,000 160,500 45,500 55,200 60,800 94,100

Orange Garden Grove city 172,900 178,200 46,200 48,200 51,700 58,500

Orange Huntington Beach city 193,200 207,100 74,900 81,200 75,800 87,000

Orange Irvine city 227,100 327,300 81,800 123,400 224,400 320,000

Orange Laguna Beach city 23,100 23,100 10,800 11,000 12,100 14,100

Orange Laguna Hills city 30,600 31,500 10,400 10,900 18,500 19,400

Orange Laguna Niguel city 63,900 72,000 24,300 27,700 18,300 22,100

Orange Laguna Woods city 16,500 17,100 11,400 11,700 4,400 6,500

Orange La Habra city 61,100 68,500 19,000 21,700 17,300 19,900

Orange Lake Forest city 78,500 90,700 26,300 30,500 39,200 49,000

Orange La Palma city 15,800 15,800 5,100 5,100 7,700 8,500

Orange Los Alamitos city 11,600 12,100 4,100 4,200 14,200 15,600

Orange Mission Viejo city 94,500 96,600 33,200 34,100 37,100 39,100

Orange Newport Beach city 86,300 92,700 38,800 41,700 76,000 79,100

Orange Orange city 138,500 153,000 43,600 49,300 94,100 105,500

Orange Placentia city 51,500 58,400 16,600 18,900 19,000 23,500

Orange Rancho Santa 

Margarita city 48,500 48,700 16,700 16,800 17,200 19,500

Orange San Clemente city 64,400 68,000 24,000 25,300 24,800 29,500

Orange San Juan Capistrano 

city 35,200 39,500 11,500 13,300 14,700 17,900

Table 11 Jurisdictional Forecast 2040 Continued
CURRENT CONTEXT I DEMOGRAPHICS & GROWTH FORECAST 27

County City Name 2012 

Population

 2040 

Population

 2012 

Household

 2040 

Household

2012 

Employment

 2040 

Employment

Orange Santa Ana city 329,200 343,100 73,300 78,000 154,800 166,000

Orange Seal Beach city 24,400 24,800 13,000 13,300 11,000 12,300

Orange Stanton city 38,700 41,600 10,700 11,800 7,200 8,500

Orange Tustin city 77,300 83,000 25,600 27,900 37,600 66,400

Orange Villa Park city 5,900 6,100 2,000 2,000 1,500 1,700

Orange Westminster city 91,000 92,800 26,200 26,800 24,200 26,400

Orange Yorba Linda city 66,200 70,500 21,900 23,400 15,600 17,700

Orange Unincorporated 120,700 180,100 37,800 56,900 20,700 41,200

Riverside Banning city 30,100 37,600 10,800 14,000 7,300 14,200

Riverside Beaumont city 39,400 80,600 12,400 27,200 5,900 18,000

Riverside Blythe city 20,000 24,600 4,500 6,200 3,700 6,600

Riverside Calimesa city 8,100 24,800 3,300 10,900 1,300 5,900

Riverside Canyon Lake city 10,700 11,300 3,900 4,100 1,200 2,700

Riverside Cathedral City city 52,200 68,100 17,100 26,000 10,800 21,200

Riverside Coachella city 42,400 146,300 9,200 40,100 8,500 34,400

Riverside Corona city 156,000 172,300 45,300 52,000 66,400 88,400

Riverside Desert Hot Springs city 27,800 58,900 9,100 21,900 3,700 12,900

Riverside Eastvale City 56,500 65,400 14,100 16,500 4,300 9,800

Riverside Hemet city 80,800 126,500 30,300 52,200 21,000 45,500

Riverside Indian Wells city 5,100 7,200 2,800 4,400 4,000 7,000

Riverside Indio city 78,800 123,300 23,800 39,300 16,000 36,800

Riverside Lake Elsinore city 54,100 111,400 15,200 35,000 11,800 31,700

Riverside La Quinta city 38,300 47,700 14,900 19,100 12,400 21,500

Riverside Menifee city 81,600 121,100 28,400 48,100 10,300 23,500

Riverside Moreno Valley city 197,600 256,600 51,800 73,000 31,400 83,200

Riverside Murrieta city 105,600 129,800 32,800 43,500 23,200 45,100

Riverside Norco city 26,900 32,100 7,000 9,200 13,200 25,700

Riverside Palm Desert city 49,800 61,700 23,400 31,400 36,900 53,600

Riverside Palm Springs city 45,600 56,900 22,900 31,300 26,300 45,800

Riverside Perris city 70,700 116,700 16,600 32,700 15,100 32,200

Riverside Rancho Mirage city 17,600 25,000 8,900 13,600 12,300 20,500

Riverside Riverside city 310,700 386,600 92,400 118,600 120,000 200,500

Table 11 Jurisdictional Forecast 2040 Continued
 28 2016–2040 RTP/SCS I APPENDIX

County City Name 2012 

Population

 2040 

Population

 2012 

Household

 2040 

Household

2012 

Employment

 2040 

Employment

Riverside San Jacinto city 45,100 79,900 13,200 27,600 5,900 17,800

Riverside Temecula city 104,100 137,400 32,500 42,900 43,000 63,500

Riverside Wildomar city 33,000 56,200 10,100 18,100 5,000 13,500

Riverside Jurupa Valley City 97,000 114,500 25,000 30,400 24,500 32,600

Riverside March JPA* 500 4,000 400 2,100 700 3,600

Riverside Unincorporated 359,000 499,200 112,300 162,900 70,500 156,600

San Bernardino Adelanto city 31,100 70,000 7,900 18,100 3,900 7,800

San Bernardino Apple Valley town 70,200 100,600 23,700 34,800 15,400 27,600

San Bernardino Barstow city 23,100 35,100 8,100 12,900 8,100 16,800

San Bernardino Big Bear Lake city 5,100 6,900 2,200 3,000 3,800 5,400

San Bernardino Chino city 79,400 120,400 21,000 34,000 42,600 50,600

San Bernardino Chino Hills city 75,800 94,900 23,000 28,300 11,500 18,600

San Bernardino Colton city 52,800 69,100 15,000 20,800 16,800 29,200

San Bernardino Fontana city 200,200 280,900 49,600 74,000 47,000 70,800

San Bernardino Grand Terrace city 12,200 14,200 4,400 5,700 2,200 5,300

San Bernardino Hesperia city 91,100 129,100 26,400 39,100 14,900 28,300

San Bernardino Highland city 53,700 66,900 15,500 20,600 5,500 10,200

San Bernardino Loma Linda city 23,400 29,300 8,800 11,800 16,700 21,100

San Bernardino Montclair city 37,200 42,700 9,600 11,600 16,500 19,000

San Bernardino Needles city 4,900 7,000 1,900 2,800 2,200 3,800

San Bernardino Ontario city 166,300 258,600 45,100 75,300 103,300 175,400

San Bernardino Rancho Cucamonga 

city 170,100 204,300 55,400 73,100 69,900 104,600

San Bernardino Redlands city 69,600 85,500 24,800 32,400 31,700 53,400

San Bernardino Rialto city 100,800 112,000 25,400 31,500 21,100 30,500

San Bernardino San Bernardino city 211,900 257,400 59,300 77,100 88,900 128,900

San Bernardino Twentynine Palms city 25,900 37,300 8,300 11,400 4,300 8,500

San Bernardino Upland city 74,700 81,700 25,900 28,900 31,700 43,500

San Bernardino Victorville city 119,600 184,500 33,100 55,400 29,800 52,700

San Bernardino Yucaipa city 52,300 72,500 18,400 28,200 8,200 15,000

San Bernardino Yucca Valley town 21,000 26,300 8,300 12,200 6,100 10,000

San Bernardino Unincorporated 295,600 344,100 94,200 111,300 57,400 91,100

Table 11 Jurisdictional Forecast 2040 Continued

*The March JPA (Joint Powers Authority) is designated as the federally recognized reuse authority for the former active duty base. 
CURRENT CONTEXT I DEMOGRAPHICS & GROWTH FORECAST 29

County City Name 2012 

Population

 2040 

Population

 2012 

Household

 2040 

Household

2012 

Employment

 2040 

Employment

Ventura Camarillo city 66,300 79,900 24,800 30,200 35,800 47,300

Ventura Fillmore city 18,800 21,800 5,200 6,300 3,000 5,300

Ventura Moorpark city 34,800 43,000 10,600 13,100 11,300 16,600

Ventura Ojai city 7,500 8,400 3,100 3,300 5,100 5,300

Ventura Oxnard city 200,100 237,300 50,100 60,100 58,100 79,200

Ventura Port Hueneme city 21,800 22,400 7,100 7,300 6,400 6,700

Ventura San Buenaventura 

(Ventura) city 106,700 125,300 40,700 48,400 60,700 66,000

Ventura Santa Paula city 29,800 39,600 8,500 11,500 7,800 11,700

Ventura Simi Valley city 125,100 142,400 41,300 47,400 44,000 61,100

Ventura Thousand Oaks city 127,800 131,700 45,900 47,200 68,200 81,900

Ventura Unincorporated 96,700 113,600 32,100 37,500 31,800 38,700

Table 11 Jurisdictional Forecast 2040 Continued

Note: All figures are rounded to the nearest 100.

*The March JPA (Joint Powers Authority) is designated as the federally recognized reuse authority for the former active duty base. 
 30 2016–2040 RTP/SCS I APPENDIX

TAZ LEVEL PROJECTIONS

The socioeconomic input data for the transportation model are processed at the 

Transportation Analysis Zone (TAZ) level in two different formats: 

1. The marginal total of person and household attributes and 

2. The joint distributes of person and household attributes. TAZ is often referred 

to as TIER 2, are generally equivalent to Census block groups, and there are 

11,267 TAZs in the region.

A total of 65 socioeconomic variables and 8 joint tables are developed as input for the 

transportation demand model (see TABLE 12 and TABLE 13). These variables include 

population, households by type, household income by category, employment by sector, 

etc. The eight joint tables, each with two or more dimensional attributions, are required by 

SCAG’s enhanced transportation demand model. One of these joint distributions is number 

of households by household income, household size, the number of workers and the type of 

dwelling units, at the TAZ level.

SCAG develops the marginal and joint distribution of socioeconomic attributes at the TAZ 

level using diverse public and private sources of data and advanced estimation methods. 

The major data sources include the 2000 and 2010 Census, 2006-2010 Census 

Transportation Planning Package (CTPP), American Community Survey (ACS), California 

Department of Finance (DOF), California Employment Development Department (EDD), Firm 

based info Group 2011, Existing Land Use, 2012 County Assessor’s Parcel Database, and 

jurisdictional general plans.

The development of the TAZ level socioeconomic input involves three major processes:

1. Development of three major variables: population, households, and employment;

2. Development of secondary variables: socioeconomic attributes of persons, 

households, and employment sectors;

3. Development of joint distributions of selected attributes.

The TAZ level projections are all consistent to the local general plan capacity.

DEVELOPMENT OF MAJOR VARIABLES

The initial TAZ level household projections started from the household and employment 

estimates at the Minimum Planning Unit (MPU) level within the TAZ. The MPU is the 

smallest geographic computing unit at which our calculations can take place. In general, the 

MPUs are equivalent to parcels. The 2012 parcel data, the 2010 Census and the 2011 firm 

based employment data are the key databases used for the initial MPU level household and 

employment estimates.

The aggregation of the MPU level household and employment became the first draft of the 

TAZ level estimates.

Total population is calculated based on the TAZ household forecast. The two components for 

the total population are group quarters population and residential population. The average 

number of persons per household (PPH) is projected using the recent estimates and trends, 

and is calculated using the updated jurisdictional totals for population and households. 

Group quarters population is projected relying on the censuses and historical trends.

TAZ level household and employment projections are controlled to the jurisdictional level 

projections. Which means the sum of TAZ level household and employment within a 

jurisdiction are the same as this jurisdiction’s growth projections. 

The initial TAZ level jobs are projected using a constant-share method. The current 

TAZ’s share of jurisdictional level jobs for each sector will remain constant during the 

forecast years. By using the constant share method, the TAZ’s job growth by sector will 

be simply determined by the different growth of the specific sector by a jurisdiction. The 

initial TAZ population, household, and employment forecasts become a basis for the 

local review process.

DEVELOPMENT OF SOCIAL ECONOMIC VARIABLES FOR 

TRANSPORTATION MODELS

SCAG develops additional attribute variables such as population by age, household 

by income range, employment by sector, and etc. Please refer to TABLE 12 for entire 

variable list. The joint distributions of households are developed into joint distributions 

of selected secondary variables using the Population Synthesis (PopSyn). It generates 

synthetic population and households with attribute distributions, which become the basis 

for computing the joint distributions. SCAG uses the 2010 Census SF1 (Summary File 1)

aggregated data at the TAZ level and 2007-2011 five-year PUMS (Public Use Microdata 

Sample) based individual data at the PUMA (Public Use Microdata Areas) level as seed data 

to produce synthetic population and households at the TAZ level.
CURRENT CONTEXT I DEMOGRAPHICS & GROWTH FORECAST 31

Variables Description of Variables POPULATION (8 VARIABLES)

Total Population (1 variable): total number of people living within a zone. Total 

population is composed of residential population and group quarters population.

Group Quarters (Non-Institutional) Population (1 variable): is primarily comprised of 

students residing in dormitories, military personnel living in barracks, and individuals 

staying in homeless shelters. Group quarters (non-institutional) population does 

NOT include persons residing in institutions.

Residential Population (1 variable): the number of residents NOT living in “group 

quarters”.

Group Quarters Population living in student dormitories (1 variable): Population living 

in college dormitories (includes college quarters off campus).

Population by Age (4 variables): the number of population for different age groups: 

5-17, 18-24, 16-64, and 65+.

HOUSEHOLDS 

(26 VARIABLES)

Total Households (1 variable): Household refers to all of the people who occupy a 

housing unit. By definition there is only one household in an occupied housing unit.

Households by Household Size (4 variables): the number of one-person households, 

two-person households, three-person households, and four or more person 

households.

Households by Age of Householder (4 variables): the number of households with age 

of householder between 18 and 24 years old, 25 and 44, 45 and 64, and 65 or older.

Households by Number of Workers (4 variables): the number of households with no 

worker, with one worker, with two workers, and with 3+ worker.

Households by Household Income (4 variables): the number of households with 

annual household income (in 2011 dollar) of less than $35,000, $35,000-74,999, 

75,000- 149,999, and 150,000 or more.

Households by Type of Dwelling Unit (2 variables): the number of households living 

in single-family detached housing, and living in other housing.

Households by Number of College Students (3 variables): the number of households 

with no college student, with one college student, with two college students or more.

Households by Number of Children age 5-17 (4 variables): the number of households 

with no child, with one child, with two children, and three children or more.

SCHOOL ENROLLMENT

(2 VARIABLES)

K-12 School Enrollment (1 variable): the total number of K-12 (kindergarten through 

12th grade) students enrolled in all public and private schools located within a zone. 

All elementary, middle (junior high), and high school students are included. This 

variable represents “students by place of attendance”.

College/University Enrollment (1 variable): the total number of students enrolled 

in any public or private post-secondary school (college or university) that grant 

an associate degree or higher, located within a zone. This variable also represents 

“students by place of attendance”.

Table 12 Description of Socioeconomic Variables

Variables Description of Variables WORKERS (4 VARIABLES):

Total Workers (1 variable): total number of civilian workers residing in a zone. Workers 

are estimated by the place of residence.

Workers by earning level (3 variables): the number of workers with earnings of less 

than $35,000, $35,000-$74,999, and $75,000 or more.

MEDIAN HOUSEHOLD 

INCOME (5 VARIABLES):

Median Household Income (1 variable): median household income is the median 

value of household income for all households within a zone. Household Income 

includes the income, from all sources, for all persons aged 15 years or older within a 

household.

Median Household Income by Income Categories (4 variables): the median income is 

estimated for each of four different income categories: less than $35,000, $35,000-

$74,999, $75,000-$149,999, and $150,000 or more.

EMPLOYMENT 

(17 VARIABLES)

Total Employment (1 variable): total number of jobs including full and part-time within 

a zone. The employment variables represent all jobs located within a zone (i.e., 

employment by place of work). Jobs are composed of wage and salary jobs and selfemployed jobs. Jobs are categorized into 13 sectors based on the North American 

Industry Classification

System (NAICS) code definition.

Employment by 13 Industries (13 variables): the number of total jobs for 

1) agriculture & mining, 

2) construction, 

3) manufacturing, 

4) wholesale trade, 

5) retail trade, 

6) transportation, warehousing, and utilities, 

7) information, 

8) financial activities, 

9) professional and business services, 

10) education and health services, 

11) leisure and hospitality services, 

12) other services, and 

13) public administration.

Employment related variables: 

1. Light/General warehouse area

2. High cube warehouse area

3. Light/General warehouse employment

4. High cube warehouse employment

Employment by wage level (3 variables): total number of jobs by three wage levels:

of less than $35,000, $35,000 to $74,999, $75,000 or more. 

TABLE 12 Description of Socioeconomic Variables Continued
 32 2016–2040 RTP/SCS I APPENDIX

Table 13 Joint Distributions of Population, Households, and Workers by Selected Demographic 

Attributes

Major 

Variables Demographic Attributes HOUSEHOLD 1

Household income (less than $35,000, $35,000 to $74,999, $75,000 to 

$149,999, $150,000 or more)

Household size (1,2,3,4 or more persons in household)

Number of workers (0,1,2,3 or more workers in household) 

Type of dwelling unit (single-family detached, other)

HOUSEHOLD 2

Household income (less than $35,000, $35,000 to $74,999, $75,000 to 

$149,999, $150,000 or more)

Number of workers (0,1,2,3 more workers in household)

Age of head of household (18-24, 25-44, 45-66, 65 or more years old).

HOUSEHOLD 3

Household income (less than $35,000, $35,000 to $74,999, $75,000 to 

$149,999, $150,000 or more)

Household size (1,2,3,4 more persons in household)

HOUSEHOLD 4

Household income (less than $35,000, $35,000 to $74,999, $75,000 to 

$149,999, $150,000 or more)

Number of college students (0, 1, 2 or more)

HOUSEHOLD 5

Household income (less than $35,000, $35,000 to $74,999,

$75,000 to $149,999, $150,000 or more)

Number of children age 5-17 (0,1,2,3 or more)

Major 

Variables Demographic Attributes HOUSEHOLD 6

Household income (less than $35,000, $35,000 to $74,999, $75,000 to 

$149,999, $150,000 or more)

Population Age (0-4, 5-17, 18-24, 25 or more)

HOUSEHOLD 7

Household income (less than $35,000, $35,000 to $74,999, $75,000 to 

$149,999, $150,000 or more)

Worker’s earnings (less than $24,999, $25,000-$49,999, $50,000 or more)

HOUSEHOLD 8

Household income (less than $35,000, $35,000 to $74,999, $75,000 to 

$149,999, $150,000 or more)

Household size (1,2,3,4 or more persons in household) 

Number of workers (0,1,2,3 or more workers in household) 

Type of dwelling unit (single-family detached, other)

Age of head of household (18-24, 25-44, 45-66, 65 or more years old)

TABLE 13 Joint Distributions of Population, Households, and Workers by Selected Demographic 

Attributes Continued
CURRENT CONTEXT I DEMOGRAPHICS & GROWTH FORECAST 33

A NOTE FOR THE MOUNTAIN AREA SEASONAL 

CHARACTERISTICS

Reporting of socio-economic data and analysis of transportation needs for the mountain 

areas of San Bernardino County are a challenge given significant seasonal variation due 

to recreation activities and tourism. SCAG’s forecast of future population, households, and 

employment for purposes of economic, infrastructure and transportation planning are built 

primarily from U.S. Census and state employment data for a “typical” season of the year. 

In the San Bernardino Mountain communities, such as the City of Big Bear Lake, or areas 

like Lake Arrowhead, Crestline, Wrightwood, and Running Springs, the full-time population 

and employment of the area are relatively low, but significant increases are experienced 

during the peak winter and summer seasons due to the added seasonal residents and 

tourism. As a result, standard socio-economic growth forecasts for these areas tend not to 

reflect the significant seasonal variations experienced due to visitor/recreational activities. 

Seasonal characteristics in mountain and some desert communities are not captured 

by conventional methods that are utilized to forecast growth and analyze transportation 

needs. Therefore, special attention must be given to these communities to acknowledge the 

unique demographic conditions and travel needs of these areas. As an example, TABLE 14 

presents peak seasonal characteristics for the City of Big Bear Lake, illustrating the impact of 

seasonal fluctuations. For transportation planning and facility design in these areas, special 

consideration and studies are required to ensure seasonal impacts are properly captured.

Population Households Employment Visitors

2012 Off-Peak 5,100 2,200 3,800 10,000

2012 Peak --- --- 5,800 60,000

2040 Off-Peak 7,000 3,000 5,400 14,000

2040 Peak --- --- 7,400 76,000

Table 14 Seasonal Comparison of Activity in the City of Big Bear Lake 

Note: Visitors and Peak Season forecasts provided by City of Big Bear Lake Planning Staff
Kern

San Diego

Camarillo

Moorpark

Ojai

Simi Valley

Thousand

Oaks

Oxnard

Arcadia Azusa

Burbank

Carson

Downey

El

Monte

Glendale

Glendora

La

Mirada

Los Angeles Monrovia

Monterey

Park

Norwalk

Pasadena

Pomona

Torrance

Walnut

West

Covina

Whittier

Calabasas

Long

Beach

Adelanto

Apple Valley

Barstow

Chino

Chino Hills

Colton

Fontana

Hesperia

Highland

Loma

Ontario Linda

Rancho

Cucamonga

Redlands

Rialto San

Bernardino

Upland

Victorville

Yucaipa

Yucca Valley

Lancaster

Palmdale

Santa Clarita

Eastvale

Lake Elsinore

Hemet 

Indian

Wells

Temecula

Perris

Moreno Valley

Murrieta

Wildomar

Rancho

Mirage

Corona

Jurupa Valley

Desert Hot Springs

San

Jacinto

Beaumont Banning

Calimesa

Palm

Desert

Riverside

Menifee

Norco

La 

Palm

Springs Anaheim

Brea

Buena

Park

Costa

Mesa

Fullerton

Garden

Grove

Huntington

Beach

Irvine

Laguna

Niguel

Mission

Viejo

Newport

Beach

Orange

San

Clemente

Santa Ana

Seal

Beach Tustin

Yorba Linda 91

14

60

126

71

57

60

110

134

91

22

134

101

101

101

15

215

10

15

10

210

405

5

605

710

5

15

405

110

105

5

10

405

210

210

San Bernardino

County

Riverside

County

Orange

County

Los Angeles

County

Ventura

County

Population Density in 2012 (Persons per Square Mile)

Less Than or Equal to 1,000

1,001 - 10,000

10,001 - 20,000

20,001 - 30,000

Greater Than 30,000

(Source: SCAG, 2015)

8

10

10

0 5 10 2.5

Miles

Note: Transportation Analysis Zone (TAZ) level data or any data at a geography

smaller than the jurisdictional level is included in the draft PGF for regional

modeling purpose only, and is advisory and non-binding.

Exhibit 1 2012 Population
Kern

San Diego

Camarillo

Moorpark

Ojai

Simi Valley

Thousand

Oaks

Oxnard

Arcadia Azusa

Burbank

Carson

Downey

El

Monte

Glendale

Glendora

La

Mirada

Los Angeles Monrovia

Monterey

Park

Norwalk

Pasadena

Pomona

Torrance

Walnut

West

Covina

Whittier

Calabasas

Long

Beach

Adelanto

Apple Valley

Barstow

Chino

Chino Hills

Colton

Fontana

Hesperia

Highland

Loma

Ontario Linda

Rancho

Cucamonga

Redlands

Rialto San

Bernardino

Upland

Victorville

Yucaipa

Yucca Valley

Lancaster

Palmdale

Santa Clarita

Eastvale

Lake Elsinore

Hemet 

Indian

Wells

Temecula

Perris

Moreno Valley

Murrieta

Wildomar

Rancho

Mirage

Corona

Jurupa Valley

Desert Hot Springs

San

Jacinto

Beaumont Banning

Calimesa

Palm

Desert

Riverside

Menifee

Norco

La 

Palm

Springs Anaheim

Brea

Buena

Park

Costa

Mesa

Fullerton

Garden

Grove

Huntington

Beach

Irvine

Laguna

Niguel

Mission

Viejo

Newport

Beach

Orange

San

Clemente

Santa Ana

Seal

Beach Tustin

Yorba Linda 91

14

60

126

71

57

60

110

134

91

22

134

101

101

101

15

215

10

15

10

210

405

5

605

710

5

15

405

110

105

5

10

405

210

210

San Bernardino

County

Riverside

County

Orange

County

Los Angeles

County

Ventura

County

Population Density in 2040 (Persons per Square Mile)

Less Than or Equal to 1,000

1,001 - 10,000

10,001 - 20,000

20,001 - 30,000

Greater Than 30,000

(Source: SCAG, 2015)

8

10

10

0 5 10 2.5

Miles

Note: Transportation Analysis Zone (TAZ) level data or any data at a geography

smaller than the jurisdictional level is included in the draft PGF for regional

modeling purpose only, and is advisory and non-binding.

Exhibit 2 2040 Population
Kern

San Diego

Camarillo

Moorpark

Ojai

Simi Valley

Thousand

Oaks

Oxnard

Arcadia Azusa

Burbank

Carson

Downey

El

Monte

Glendale

Glendora

La

Mirada

Los Angeles Monrovia

Monterey

Park

Norwalk

Pasadena

Pomona

Torrance

Walnut

West

Covina

Whittier

Calabasas

Long

Beach

Adelanto

Apple Valley

Barstow

Chino

Chino Hills

Colton

Fontana

Hesperia

Highland

Loma

Ontario Linda

Rancho

Cucamonga

Redlands

Rialto San

Bernardino

Upland

Victorville

Yucaipa

Yucca Valley

Lancaster

Palmdale

Santa Clarita

Eastvale

Lake Elsinore

Hemet 

Indian

Wells

Temecula

Perris

Moreno Valley

Murrieta

Wildomar

Rancho

Mirage

Corona

Jurupa Valley

Desert Hot Springs

San

Jacinto

Beaumont Banning

Calimesa

Palm

Desert

Riverside

Menifee

Norco

La 

Palm

Springs Anaheim

Brea

Buena

Park

Costa

Mesa

Fullerton

Garden

Grove

Huntington

Beach

Irvine

Laguna

Niguel

Mission

Viejo

Newport

Beach

Orange

San

Clemente

Santa Ana

Seal

Beach Tustin

Yorba Linda 91

14

60

126

71

57

60

110

134

91

22

134

101

101

101

15

215

10

15

10

210

405

5

605

710

5

15

405

110

105

5

10

405

210

210

San Bernardino

County

Riverside

County

Orange

County

Los Angeles

County

Ventura

County

Population Growth, 2012 - 2040 (Persons per Square Mile)

Less than or Equal to 500

501 - 1,000

1,001 - 2,500

2,501 - 5,000

Greater than 5,000

(Source: SCAG, 2015)

8

10

10

0 5 10 2.5

Miles

Note: Transportation Analysis Zone (TAZ) level data or any data at a geography

smaller than the jurisdictional level is included in the draft PGF for regional

modeling purpose only, and is advisory and non-binding.

Exhibit 3 Population Change, 2012-2040
Kern

San Diego

Camarillo

Moorpark

Ojai

Simi Valley

Thousand

Oaks

Oxnard

Arcadia Azusa

Burbank

Carson

Downey

El

Monte

Glendale

Glendora

La

Mirada

Los Angeles Monrovia

Monterey

Park

Norwalk

Pasadena

Pomona

Torrance

Walnut

West

Covina

Whittier

Calabasas

Long

Beach

Adelanto

Apple Valley

Barstow

Chino

Chino Hills

Colton

Fontana

Hesperia

Highland

Loma

Ontario Linda

Rancho

Cucamonga

Redlands

Rialto San

Bernardino

Upland

Victorville

Yucaipa

Yucca Valley

Lancaster

Palmdale

Santa Clarita

Eastvale

Lake Elsinore

Hemet 

Indian

Wells

Temecula

Perris

Moreno Valley

Murrieta

Wildomar

Rancho

Mirage

Corona

Jurupa Valley

Desert Hot Springs

San

Jacinto

Beaumont Banning

Calimesa

Palm

Desert

Riverside

Menifee

Norco

La 

Palm

Springs Anaheim

Brea

Buena

Park

Costa

Mesa

Fullerton

Garden

Grove

Huntington

Beach

Irvine

Laguna

Niguel

Mission

Viejo

Newport

Beach

Orange

San

Clemente

Santa Ana

Seal

Beach Tustin

Yorba Linda 91

14

60

126

71

57

60

110

134

91

22

134

101

101

101

15

215

10

15

10

210

405

5

605

710

5

15

405

110

105

5

10

405

210

210

San Bernardino

County

Riverside

County

Orange

County

Los Angeles

County

Ventura

County

Household Density in 2012 (Households per Square Mile)

Less Than or Equal to 400

401 - 4,000

4,001 - 8,000

8,001 - 12,000

Greater Than 12,000

(Source: SCAG, 2015)

8

10

10

0 5 10 2.5

Miles

Note: Transportation Analysis Zone (TAZ) level data or any data at a geography

smaller than the jurisdictional level is included in the draft PGF for regional

modeling purpose only, and is advisory and non-binding.

Exhibit 4 2012 Households
Kern

San Diego

Camarillo

Moorpark

Ojai

Simi Valley

Thousand

Oaks

Oxnard

Arcadia Azusa

Burbank

Carson

Downey

El

Monte

Glendale

Glendora

La

Mirada

Los Angeles Monrovia

Monterey

Park

Norwalk

Pasadena

Pomona

Torrance

Walnut

West

Covina

Whittier

Calabasas

Long

Beach

Adelanto

Apple Valley

Barstow

Chino

Chino Hills

Colton

Fontana

Hesperia

Highland

Loma

Ontario Linda

Rancho

Cucamonga

Redlands

Rialto San

Bernardino

Upland

Victorville

Yucaipa

Yucca Valley

Lancaster

Palmdale

Santa Clarita

Eastvale

Lake Elsinore

Hemet 

Indian

Wells

Temecula

Perris

Moreno Valley

Murrieta

Wildomar

Rancho

Mirage

Corona

Jurupa Valley

Desert Hot Springs

San

Jacinto

Beaumont Banning

Calimesa

Palm

Desert

Riverside

Menifee

Norco

La 

Palm

Springs Anaheim

Brea

Buena

Park

Costa

Mesa

Fullerton

Garden

Grove

Huntington

Beach

Irvine

Laguna

Niguel

Mission

Viejo

Newport

Beach

Orange

San

Clemente

Santa Ana

Seal

Beach Tustin

Yorba Linda 91

14

60

126

71

57

60

110

134

91

22

134

101

101

101

15

215

10

15

10

210

405

5

605

710

5

15

405

110

105

5

10

405

210

210

San Bernardino

County

Riverside

County

Orange

County

Los Angeles

County

Ventura

County

Household Density in 2040 (Households per Square Mile)

Less Than or Equal to 400

401 - 4,000

4,001 - 8,000

8,001 - 12,000

Greater Than 12,000

(Source: SCAG, 2015)

8

10

10

0 5 10 2.5

Miles

Note: Transportation Analysis Zone (TAZ) level data or any data at a geography

smaller than the jurisdictional level is included in the draft PGF for regional

modeling purpose only, and is advisory and non-binding.

Exhibit 5 2040 Households
Kern

San Diego

Camarillo

Moorpark

Ojai

Simi Valley

Thousand

Oaks

Oxnard

Arcadia Azusa

Burbank

Carson

Downey

El

Monte

Glendale

Glendora

La

Mirada

Los Angeles Monrovia

Monterey

Park

Norwalk

Pasadena

Pomona

Torrance

Walnut

West

Covina

Whittier

Calabasas

Long

Beach

Adelanto

Apple Valley

Barstow

Chino

Chino Hills

Colton

Fontana

Hesperia

Highland

Loma

Ontario Linda

Rancho

Cucamonga

Redlands

Rialto San

Bernardino

Upland

Victorville

Yucaipa

Yucca Valley

Lancaster

Palmdale

Santa Clarita

Eastvale

Lake Elsinore

Hemet 

Indian

Wells

Temecula

Perris

Moreno Valley

Murrieta

Wildomar

Rancho

Mirage

Corona

Jurupa Valley

Desert Hot Springs

San

Jacinto

Beaumont Banning

Calimesa

Palm

Desert

Riverside

Menifee

Norco

La 

Palm

Springs Anaheim

Brea

Buena

Park

Costa

Mesa

Fullerton

Garden

Grove

Huntington

Beach

Irvine

Laguna

Niguel

Mission

Viejo

Newport

Beach

Orange

San

Clemente

Santa Ana

Seal

Beach Tustin

Yorba Linda 91

14

60

126

71

57

60

110

134

91

22

134

101

101

101

15

215

10

15

10

210

405

5

605

710

5

15

405

110

105

5

10

405

210

210

San Bernardino

County

Riverside

County

Orange

County

Los Angeles

County

Ventura

County

Household Growth, 2012 - 2040 (Households per Square Mile)

Less than or Equal to 200

201 - 500

501 - 1,000

1,001 - 2,000

Greater than 2,000

(Source: SCAG, 2015)

8

10

10

0 5 10 2.5

Miles

Note: Transportation Analysis Zone (TAZ) level data or any data at a geography

smaller than the jurisdictional level is included in the draft PGF for regional

modeling purpose only, and is advisory and non-binding.

Exhibit 6 Household Change, 2012-2040
Kern

San Diego

Camarillo

Moorpark

Ojai

Simi Valley

Thousand

Oaks

Oxnard

Arcadia Azusa

Burbank

Carson

Downey

El

Monte

Glendale

Glendora

La

Mirada

Los Angeles Monrovia

Monterey

Park

Norwalk

Pasadena

Pomona

Torrance

Walnut

West

Covina

Whittier

Calabasas

Long

Beach

Adelanto

Apple Valley

Barstow

Chino

Chino Hills

Colton

Fontana

Hesperia

Highland

Loma

Ontario Linda

Rancho

Cucamonga

Redlands

Rialto San

Bernardino

Upland

Victorville

Yucaipa

Yucca Valley

Lancaster

Palmdale

Santa Clarita

Eastvale

Lake Elsinore

Hemet 

Indian

Wells

Temecula

Perris

Moreno Valley

Murrieta

Wildomar

Rancho

Mirage

Corona

Jurupa Valley

Desert Hot Springs

San

Jacinto

Beaumont Banning

Calimesa

Palm

Desert

Riverside

Menifee

Norco

La 

Palm

Springs Anaheim

Brea

Buena

Park

Costa

Mesa

Fullerton

Garden

Grove

Huntington

Beach

Irvine

Laguna

Niguel

Mission

Viejo

Newport

Beach

Orange

San

Clemente

Santa Ana

Seal

Beach Tustin

Yorba Linda 91

14

60

126

71

57

60

110

134

91

22

134

101

101

101

15

215

10

15

10

210

405

5

605

710

5

15

405

110

105

5

10

405

210

210

San Bernardino

County

Riverside

County

Orange

County

Los Angeles

County

Ventura

County

Employment Density in 2012 (Jobs per Square Mile)

Less than or Equal to 500

501 - 5,000

5,001 - 10,000

10,001 - 20,000

Greater Than 20,000

(Source: SCAG, 2015)

8

10

10

0 5 10 2.5

Miles

Note: Transportation Analysis Zone (TAZ) level data or any data at a geography

smaller than the jurisdictional level is included in the draft PGF for regional

modeling purpose only, and is advisory and non-binding.

Exhibit 7 2012 Employment
Kern

San Diego

Camarillo

Moorpark

Ojai

Simi Valley

Thousand

Oaks

Oxnard

Arcadia Azusa

Burbank

Carson

Downey

El

Monte

Glendale

Glendora

La

Mirada

Los Angeles Monrovia

Monterey

Park

Norwalk

Pasadena

Pomona

Torrance

Walnut

West

Covina

Whittier

Calabasas

Long

Beach

Adelanto

Apple Valley

Barstow

Chino

Chino Hills

Colton

Fontana

Hesperia

Highland

Loma

Ontario Linda

Rancho

Cucamonga

Redlands

Rialto San

Bernardino

Upland

Victorville

Yucaipa

Yucca Valley

Lancaster

Palmdale

Santa Clarita

Eastvale

Lake Elsinore

Hemet 

Indian

Wells

Temecula

Perris

Moreno Valley

Murrieta

Wildomar

Rancho

Mirage

Corona

Jurupa Valley

Desert Hot Springs

San

Jacinto

Beaumont Banning

Calimesa

Palm

Desert

Riverside

Menifee

Norco

La 

Palm

Springs Anaheim

Brea

Buena

Park

Costa

Mesa

Fullerton

Garden

Grove

Huntington

Beach

Irvine

Laguna

Niguel

Mission

Viejo

Newport

Beach

Orange

San

Clemente

Santa Ana

Seal

Beach Tustin

Yorba Linda 91

14

60

126

71

57

60

110

134

91

22

134

101

101

101

15

215

10

15

10

210

405

5

605

710

5

15

405

110

105

5

10

405

210

210

San Bernardino

County

Riverside

County

Orange

County

Los Angeles

County

Ventura

County

Employment Density in 2040 (Jobs per Square Mile)

Less than or Equal to 500

501 - 5,000

5,001 - 10,000

10,001 - 20,000

Greater Than 20,000

(Source: SCAG, 2015)

8

10

10

0 5 10 2.5

Miles

Note: Transportation Analysis Zone (TAZ) level data or any data at a geography

smaller than the jurisdictional level is included in the draft PGF for regional

modeling purpose only, and is advisory and non-binding.

Exhibit 8 2040 Employment
Kern

San Diego

Camarillo

Moorpark

Ojai

Simi Valley

Thousand

Oaks

Oxnard

Arcadia Azusa

Burbank

Carson

Downey

El

Monte

Glendale

Glendora

La

Mirada

Los Angeles Monrovia

Monterey

Park

Norwalk

Pasadena

Pomona

Torrance

Walnut

West

Covina

Whittier

Calabasas

Long

Beach

Adelanto

Apple Valley

Barstow

Chino

Chino Hills

Colton

Fontana

Hesperia

Highland

Loma

Ontario Linda

Rancho

Cucamonga

Redlands

Rialto San

Bernardino

Upland

Victorville

Yucaipa

Yucca Valley

Lancaster

Palmdale

Santa Clarita

Eastvale

Lake Elsinore

Hemet 

Indian

Wells

Temecula

Perris

Moreno Valley

Murrieta

Wildomar

Rancho

Mirage

Corona

Jurupa Valley

Desert Hot Springs

San

Jacinto

Beaumont Banning

Calimesa

Palm

Desert

Riverside

Menifee

Norco

La 

Palm

Springs Anaheim

Brea

Buena

Park

Costa

Mesa

Fullerton

Garden

Grove

Huntington

Beach

Irvine

Laguna

Niguel

Mission

Viejo

Newport

Beach

Orange

San

Clemente

Santa Ana

Seal

Beach Tustin

Yorba Linda 91

14

60

126

71

57

60

110

134

91

22

134

101

101

101

15

215

10

15

10

210

405

5

605

710

5

15

405

110

105

5

10

405

210

210

San Bernardino

County

Riverside

County

Orange

County

Los Angeles

County

Ventura

County

Employment Growth, 2012 - 2040 (Jobs per Square Mile)

Less than or Equal to 200

201 - 500

501 - 1,000

1,001 - 2,000

Greater than 2,000

(Source: SCAG, 2015)

8

10

10

0 5 10 2.5

Miles

Note: Transportation Analysis Zone (TAZ) level data or any data at a geography

smaller than the jurisdictional level is included in the draft PGF for regional

modeling purpose only, and is advisory and non-binding.

Exhibit 9 Employment Change, 2012-2040
 43

REFERENCES

Field, Brian, and B. MacGregor. 1987. Forecasting Techniques for Urban and Regional 

Planning. London: UCL Press.

Plane, David, and P. Rogerson. 1994. The Geographical Analysis of Population: With 

Applications to Planning and Business. New York: Wiley.
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ADOPTED | APRIL 2016

WWW.SCAGRTPSCS.NET

APPENDIX

CURRENT CONTEXT I DEMOGRAPHICS & GROWTH FORECAST

please recycle 2347 2016.03.31

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