THE UCLA ANDERSON FORECAST FOR THE NATION ......Sources: Federal Reserve Board and UCLA Anderson...

June 2015 Report

THE UCLA ANDERSON FORECAST FOR THE NATION AND CALIFORNIA

FORECASTS:2015 2nd Quarter2017 4th Quarter

64th Year

UCLA Anderson Forecast

Director:Edward E. LeamerProfessor of Global Economics and Management and Chauncey J. Medberry Chair in Management

The UCLA Anderson Forecast Staff:Jerry Nickelsburg, Senior Economist, Adjunct Professor of Economics, UCLA Anderson School David Shulman, Senior EconomistWilliam Yu, EconomistPatricia Nomura, Economic Research and Managing EditorEydie Grossman, Director of Business Development George Lee, Publications and Marketing Manager

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This forecast was prepared based upon assumptions reflecting the Project’s judgements as of the date it bears. Actual results could vary materially from the forecast. Neither the UCLA Anderson Forecast nor The Regents of the University of California shall be held responsible as a consequence of any such variance. Unless approved by the UCLA Anderson Forecast, the publication or distribution of this forecast and the preparation, publication or distribution of any excerpts from this forecast are prohibited.

Published quarterly by the UCLA Anderson Forecast, a unit of UCLA Anderson School of Management.

Copyright 2015 by the Regents of the University of California.

The Quarterly Forecast:

“Tech and the California Economy: Is the Future in the Code?”

Upcoming Events:

Fall Quarterly Conference - Real Estate September 28, 2015Winter Quarterly Conference December 2015Orange County Economic Outlook for 2015 April 2016Summer Conference June 2016

June 2015 Report

THE UCLA ANDERSON FORECAST FOR THE NATION AND CALIFORNIA

Nation California

A Bump on the Road to 3% Growth 11David Shulman

We're in a Race With Technology 17Scott McGregor Broadcom Corporation President and Chief Executive Officer

Focusing on the Planet's Most 19Precious Resources Naveen JainFounder, Moon Express, INome, World Innovation Institute

Charts 21Recent Evidence

Charts 26Forecast

Tables 35Short-Term

Tables 39Detailed

When Will California Reach its Potential 51 (Employment)? Jerry Nickelsburg

Silicon Beach and the 59Los Angeles Economy William Yu

The Evolution of City Human Capital 69Index Across the Country and Public School Performance in California: William Yu

Charts 97Recent Evidence

Charts 102Forecast

Tables 109Summary

Tables 113Detailed

JUNE 2015 REPORT

THE UCLA ANDERSON FORECAST FOR THE NATION

A Bump on the Road to 3% Growth

We're in a Race With Technology

Focusing on the Planet's Most Precious Resources

UCLA Anderson Forecast, June 2015 Nation–11

A BUMP ON THE ROAD TO 3% GROWTH

A Bump on the Road to 3% GrowthDavid ShulmanSenior Economist, UCLA Anderson ForecastJune 2015

Just like last year, the economy hit a weather-induced speed bump in the first quarter. Nonetheless we believe that the economy will be back on its 3% real growth path by the third quarter and continue at that pace through 2016. (See

Figure 1) At that rate of growth the economy will likely be generating jobs at a 250,000/month clip and the unemploy-ment rate will close out the year at just below 5%. (See Figures 2 and 3)

Figure 1 Real GDP Growth, 2007Q1 -2017Q4F

Source: Sources: U.S. Department of Commerce and UCLA Anderson Forecast

201720152013201120092007

6%

4%

2%

0%

-2%

-4%

-6%

-8%

-10%

(Percent Change, SAAR)

Figure 2 Payroll Employment, 2007Q1 – 2017Q4F

Sources: U.S. Bureau of Labor Statistics and UCLA Anderson Forecast

201720152013201120092007

150

145

140

135

130

125

(Millions)

12–Nation UCLA Anderson Forecast, June 2015


The Oil/Gas Price Conundrum

Although oil prices have rebounded from the first quarter low they remain roughly half the peak levels achieved in 2014. (See Figure 4) As we previously noted, we expected the economic response to be a sharp decline in oil drilling activity and a surge in consumer spending. Thus far, we have gotten the drop in oil-related capital spending where we forecast a $45 billion decline from peak to trough at an annual rate. (See Figure 5)

However, we have yet to see the approximately $150 billion annualized reduction in gasoline prices to flow through to consumer spending. (See Figure 6) Simply put, instead of spending, it appears that cash strapped consum-ers are paying down debt and increasing their savings. It’s a thought echoed by Wal-Mart in its latest conference call with investors which highlighted the soft retail environment. Needless to say, this behavior is not accord with the historical economics playbook. Nevertheless, for the time being, we are assuming that the “tax-cut effect” of lower gas prices will gradually find its way into higher consumer spending.

Figure 3 Unemployment Rate, 2007Q1 -2017Q4F


201720152013201120092007

10%

9%

8%

7%

6%

5%

4%

(Percent)

Figure 4 West Texas Intermediate Crude Oil Price, 2007Q1 – 2017Q4F

Source: Commodity Research Bureau and UCLA Anderson Forecast

201720152013201120092007

$140

$120

$100

$80

$60

$40

$20

(Dollars/Barrel)

Figure 5 Real Gross Investment in Mines and Wells, 2014-2017Q4F

Sources: U.S. Department of Commerce and UCLA Anderson Forecast

201720152013201120092007

$160

$140

$120

$100

$80

$60

$40

(Billions 2009$)



Figure 6 Real Consumption spending, 2007Q1-2017Q4


201720152013201120092007

6%

4%

2%

0%

-2%

-4%

-6%


201720152013201120092007

6%

4%

2%

0%

-2%

(Percent Change Year Ago)

Headline Core

Figure 7 Consumer Price vs. Core CPI, 2007Q1-2017Q4, %CHYA


Figure 9 Trade weighted Dollar with Majority Currency Part-ners, 2007Q1-2017Q4F

Sources: Federal Reserve Board and UCLA Anderson Forecast

201720152013201120092007

1.20

1.15

1.10

1.05

1.00

0.95

0.90

0.85

(2009=1.00)

Simply put, service prices and especially residential rents are rising in excess of 2%. Underpinning the increase in service prices will be the early signs of increasing wages across most of the economy with notable pressure on low-end wages. As we noted in the past, increases in state mandated minimum wages and the actions by such large employers as Wal-Mart, Target and Aetna are accelerating this trend. Thus, we would not be surprised to see hourly total compensation rising at a 4% pace by yearend. (See Figure 8) This domestic pressure will more than offset the fall in import prices coming from the stronger dollar. (See Figure 9)

Inflation and the Fed

With the unemployment rate essentially at the Fed’s target, all eyes are now focused on inflation. It has been our view for a while that the core inflation rate, that is, the rate of inflation excluding food and energy prices, would soon be above 2% and that the headline rate will be there too once oil prices begin to rise, which they have. (See Figure 7) This data will put to rest all of the over-hyped deflation talk we heard earlier in the year. As a result, the quarterly run-rate for inflation by both measures will be in excess of 2% by the third quarter of this year and it will be there on a year-over-year basis by the first quarter of 2016.

figure 8 Total Compensation per Hour, 2007Q1-2017Q4

Sources: Bureau of Labor Statistics and UCLA Anderson Forecast

201720152013201120092007

6%

5%

4%

3%

2%

1%

0%

-1%

(Total Compensation, %CHYA)



Thus, with the Fed’s dual mandate soon to be met, the only question remains is the timing the move off the zero interest rate policy that has been in effect since December 2008. As recently as last quarter we thought the Fed would move in June. However, given the recent soft patch in the economy our best guess is now September. We do not believe that a modest increase in the federal funds rate from zero to 25 basis points will be traumatic. As Fed Vice-Chairman Stanley Fischer recently noted, “we are going to be chang-ing monetary policy from the most extremely expansion-ary we’ve been able to do in all of history to an extremely expansionary monetary policy.”1

Thereafter we forecast that the Fed will only gradually increase the funds rate at a pace consistent with 25 basis point increases at every other meeting of the Open Market Committee. (See Figure 10) Concomitantly, longer-term interest rates rise as well, albeit at a slower pace with the yield on 10-year U.S. Treasury bonds reaching 4% in 2017.

For those who fear the impact of higher short-term interest rates on the stock market, we would remind them that history suggests that it takes several rate hikes to cause

a significant correction in stock prices. Moreover, although the Fed takes into account the effect of asset prices on the economy, it would be a mistake to believe that Fed Chair Janet Yellen is the stock market’s fairy godmother.

Figure 11 Yield on German Bonds, May 31, 2014 – May 15, 2015

Source: TradingEconomics.com

Figure 10 Federal Funds vs. 10-Year U.S. Treasury Bonds, 2007Q1 – 2017Q4F

Sources: Federal Reserve Board and UCLA Anderson Forecast

201720152013201120092007

6%

5%

4%

3%

2%

1%

0%

-1%

(Rates)

Fed Funds 10-Yr. T-bonds



Keeping a lid on long-term interest rates will be the unusually low interest rates now in evidence in Europe. As recently as April, the yield on 10- year German Bunds approached zero! Since then they have more “normalized” to about 60 basis points. Nonetheless, as deflationary fears in Europe abate and the early signs of growth we are now witnessing become realized, we expect interest rates in Europe to rise as well.

Strength to Come from Housing and Equipment Investment

Although we may have jumped the gun a bit last quarter given the poor weather in most of the country, we continue to believe that the housing market is strengthening. Strong jobs gains, increased household formations, still low interest rates, the easing of mortgage underwriting standards and modest price increases will lead to a robust construction market. Housing starts are forecast to increase to 1.16 million starts this year and 1.37 million starts next year compared to 2014’s start level of 1.00 million units. (See Figure 12)

Indeed housing starts rebounded in April to 1.14 mil-lion units on a seasonally adjusted annual rate basis making it the highest monthly total since 2007. Part of the gain repre-sented a catch up from the weather induced sub-one million unit annual rate of starts in the prior two months. Moreover, with rents rising well above the rate of increase in consumer prices, recent shift from ownership to rental units will abate leading to higher single-family and condominium starts.

On the business side, we forecast that investment in equipment will rebound from a 5.7% overall growth rate this year to 8.7% in 2016. (See Figure 13) Our confidence in that eventuality stems from the waning of the negative effects coming from the drop in oil-related capital spending and the need for business to invest in the technologies being embedded in a wide range of capital goods. That spending along with housing will provide the fuel for our 3% real growth forecast going forward.

Figure 12 Housing Starts, 2007Q1-2017Q4F, Thousands of Units, SAAR

Source: U.S. Bureau of the Census and UCLA Anderson Forecast

201720152013201120092007

1600

1400

1200

1000

800

600

400

(Thousands of Units, SAAR)

Sources: U.S. Department of Commerce and UCLA Anderson forecast

Figure 13 Investment in Equipment, 2007Q1 -2017Q4F

201720152013201120092007

30%

20%

10%

0%

-10%

-20%

-30%

-40%

-50%




Military Spending to Increase

For the past year we have argued that increased global tensions will reverse the four year decline in military spend-ing. (See Figure 14) We see nothing on the horizon to change our thinking. If anything, we are concerned that the very modest increases we are forecasting are too low. In fits and starts Congress is moving in a more “hawkish” direction.

Conclusion

Despite the first quarter bump in the road, we think the economy remains on track to a 3% growth path for real GDP. As consumer spending begins to reflect the decline in gasoline prices, the plunge in oil-related capital spend-ing abates and then reverses, housing starts and equipment spending rise, the underpinnings are there for moderate economic growth. In this environment, the unemployment rate will drop below 5%, inflation will move above 2% and the Fed will embark on a gradual tightening process starting this September.

Figure 14 Real Defense Purchases, 2007Q1 -2017Q4F, Annual Data


201720152013201120092007

8%

6%

4%

2%

0%

-2%

-4%

-6%

-8%

(Percent Change)

Endnotes

1. CNBC transcript, “Squawk on the Street” with Sara Eisen, April 16, 2015, 11:20AM.

UCLA Anderson Forecast, June 2015 Nation-17

GUEST CONTRIBUTOR: TECH IN THE CALIFORNIA ECONOMY

Fire, wheels, steam power, electricity, computers: History tells us that each new technological revolution trig-gers difficult disruptions before ultimately leading to greater economic and social gains. With the Internet of Things, the changes will come faster and be more disruptive than any technological shift the world has experienced.

The Internet of Things — the next generation of the Internet — enables dynamic data exchanges among people, devices, sensors, systems and services. It generates torrents of real-time data that can be analyzed and acted upon for greater efficiency and to respond more quickly to changing market conditions. In the Internet of Things era, success will require the ability to move fast and adapt to market conditions.

It is only just starting, but we are already beginning to see the implications. Boundaries between home and office are blurring. Jobs are moving overseas or are being replaced by robots, autonomous vehicles and automated systems. Business models are increasingly dependent on the collec-tion and analysis of data. Businesses with massive payrolls and infrastructures are dinosaurs, giving way to nimble, connected companies with fewer employees.

Economists call this kind of disruption “creative destruction,” and we’ve seen it before: 150 years ago, most jobs in America were agricultural, and most Americans lived on farms. Today, because of technological advances, only 2 percent of jobs are agricultural, most people live in cities, and yet we produce far more food than ever before.

As a society we had more than a century to adapt to the last disruptions. Millions of farmers sadly lost their jobs and land, but the high school system expanded to teach a new literacy required for office work. Farmland gave way to cities and highways, roads and waterways carried local goods to faraway markets, and communications systems — radios, television, telephones — made connectivity an essential part of work and life.

But 21st century innovation is constantly accelerating, and we don’t have a century to adapt to this next technologi-cal revolution. We are in a race with technology to develop new business models and to prepare tomorrow’s workforce with the skills they will need to thrive in the Internet of Things era.

While a college diploma was once a ticket to the middle class, today it is not even a guarantee of employment. Basic college degrees are being devalued as the supply of graduates outpaces job creation, and job creation isn’t keep-ing up with job destruction.

We’re on the verge of a mass extinction event for 20th century companies with massive infrastructure and tens of thousands of employees. The average number of years a company spends on the S&P 500 is approaching 15 years, down from 61 years in 1950. One Yale economist predicts that 75 percent of the companies on the S&P 500 today won’t be there in 10 years.

We're in a Race With TechnologyScott McGregor Broadcom Corporation President and Chief Executive OfficerGuest ContributorJune 2015



Instead, we’re seeing billion-dollar companies be-ing formed seemingly overnight with a relative handful of employees. Facebook paid $19 billion for a company called WhatsAp. WhatsAp had just 55 employees.

Is this a sign of another tech bubble? Perhaps. But even outside the tech industry, the trends are clear - valua-tions and profits are soaring, but employment is not. Some economists think we’re approaching the day when a billion-dollar company can be formed with no employees at all.

Employment growth will continue for high-income jobs that require creative cognitive skills, and for low-income service jobs. But routine, middle-income jobs, including middle management, will be hollowed out. Jobs that can be digitized will be digitized.

Each new technological era demands a new kind of literacy. In the Internet of Things era, the fastest job growth and salaries will go to workers with advanced science,

technology, engineering and applied mathematics (STEM) skills, and to business managers who can manage rapid change. Education and training can’t stop at high school or college graduation. Our education system has to adapt for these new realities.

We know from history that each new technological age brings disruption, but we also know that if we adapt suc-cessfully, each new era ultimately leads to greater prosperity and a better standard of living for all.

The Internet of Things, the expansion of global net-works, massive data centers, machine learning, and artificial intelligence will help to address some of the most pressing challenges we face as a society, including energy, health-care, transportation, education and food production … but only if we win the race with technology, to minimize the destructive aspects of creative destruction, and to prepare for the changes ahead.

UCLA Anderson Forecast, June 2015 Nation-19


Focusing on the Planet's Most Precious ResourcesNaveen JainFounder, Moon Express, INome, World Innovation InstituteGuest ContributorJune 2015

If the world’s biggest challenges are also the biggest opportunities, the time is ever so ripe for ‘big dreamers.’ These may be the very entrepreneurs whose creative and curious minds are primed to think that there are no problems that innovations and their entrepreneurial skills can’t solve.

In order to make the biggest impact, entrepreneurs

may want to target the areas that are currently primed for disruption: genetics, synthetic biology and space exploration.

Take the future of space exploration, where much

attention is paid to the idea that the space is abundant of things that are valuable or rare on or in this planet, such as

water, platinum, diamond, etc. The prospect of bringing this abundance to our planet is quite exciting, and is sure to become a game-changer for the future. The future may see the humanity settling down in Moon or Mars, just like staying in Los Angeles, New York or United Kingdom.

Further, consider the turning point when we would be

able to predict how our brains would function in the next few years. Decoding and reprogramming of human brain may become a possibility in the future. And, with several concepts that were earlier touted as science fiction becom-ing a reality, the whole system of learning is on the verge of a transformation.

The world's biggest challenges are also the biggest opportunities. Here's why we need more people who are just a little bit crazy to tackle them.

JUNE 2015 REPORT


Charts

CHARTS – RECENT EVIDENCE


15141312111009080706050403

10

5

0

-5

-10

(% Change Year Ago)

Price InflationConsumer vs. Producers' Price Index

Jan. 2003 to April 2015

Consumer Prices Producer Prices-Fin. Goods 151413121110090807060504030201

65432

10

-1

(Percent)

Interest Rates3-Mo. T-Bills vs. Long Gov't Bond Yields


3-MonthLong Gov'ts

1514131211100908070605040302

14

12

10

8

6

4

2

(Mil. Units)

Automobile SalesJan. 2002 to April 2015

CarsTrucks

1514131211100908070605040302

130

120

110

100

90

80

(Index 2004=100)

Composite Indexes of Economic IndicatorsJan. 2002 to April 2015

LeadingCoincident



15141312111009080706050403

142000

140000

138000

136000

134000

132000

130000

128000

(Thous.)

Total Nonfarm EmploymentJan. 2003 to April 2015

151413121110090807060504

140

120

100

80

60

40

20

($/Barrel)

Crude Oil PriceWest Texas IntermediateJan. 2004 to April 2015

15141312111009080706050403

10.0

9.1

8.1

7.2

6.3

5.4

4.4

3.5

(Percent)

Rate of UnemploymentJan. 2003 to April 2015

15141312111009080706050403

400

350

300

250

200

150

100

(Bil. $)

Retail SalesJan. 2003 to April 2015



15141312111009080706050403

2.5

2.0

1.5

1.0

0.5

0.0

(Mil. Units)

Housing StartsJan. 2003 to April 2015

15141312111009080706050403

1400

1200

1000

800

600

400

200

(Thous.)

Single-Family New Home SalesJan. 2003 to April 2015

15141312111009080706050403

0.950.900.850.800.750.700.650.60

130

120110

100

90

80

70

(Deutschmark/$) (Yen/$)

Japanese and European Exchange Rates


Euro/U.S. $ (Left) Yen/U.S. $ (Right)15141312111009080706050403

76543210

-1

(Index Jan.'90 = 1.00)

U.S., Japanese and GermanStock Markets

Jan. 2003 to May 2015

U.S. Japan Germany

CHARTS – FORECAST


2017201320092005200119971993

8

6

4

2

0

-2

-4

(4-Qtr. % Ch.)Real Disposable Income and Consumption

Consumption Disposable Income2017201520132011200920072005200320011999

10

8

6

4

2

0

(3-Yr. % Ch.)

Consumer Expenditures on Medical Services:Quantity % + Price % = Expenditure %

Quantity Price

2017201420112008200520021999199619931990

15

10

5

0

-5

(4-Qtr. % Ch.)Real Export and Import Growth

Exports Imports201720142011200820052002199919961993

6

5

4

3

2

1

0

(5-Yr. % Ch.)

Real GDP GrowthDeveloped World vs. U.S.

U.S. Developed World

CHARTS – FORECAST


2017201420112008200520021999199619931990

6

4

2

0

-2

-4

-6

(4-Qtr. % Ch.)Real GDP Growth

2017201420112008200520021999199619931990

18000

16000

14000

12000

10000

8000

(Bil. 2009 $)

Actual Real GDPVs. Potential Real GDP

Actual Real GDP Potential Real GDP

201720122007200219971992198719821977

10

8

6

4

2

0

(Percent)

Defense SpendingAs A Share of GDP

20172015201320112009200720052003200119991997

875421

-1-3-4

(% Ch. 12-Qtr. Mov. Avg.)

Real Purchases of Goods and Servicesby the Federal Government

CHARTS – FORECAST


20172014201120082005200219991996

0.8

0.6

0.4

0.2

0.0

-0.2

-0.4

(% of Real GDP)

Change in Real Business Inventories(3-yr. Moving Average)

20172014201120082005200219991996

30

20

10

0

-10

-20

(3-yr. % Ch.)

Real Investment-Equipment & SoftwareInfo. Processing Equip. vs. Other Equip.

Total Less Info. Equip. Information Processing Equip.

2017201420112008200520021999

14.514.013.513.012.512.011.511.0

50

48

46

44

42

40

38

(Percent) (Percent)

Nonres. Fixed Investment Share of Real GDP Vs.Equip. & Software Share of Bus. Fixed Invest.

Nonres. Fixed Investment ShareEquip. & Software Share/Nonres.Fixed

20172014201120082005200219991996

10

5

0

-5

-10

-15

(3-Yr. % Ch.)

Real Investment in Nonresidential StructuresTotal vs. Commercial Bldgs.

Total Commercial Bldgs.

CHARTS – FORECAST


2017201320092005200119971993

15141312111098

8

6

4

2

0

-2

(Invest. Share %) (4-Qtr. % Ch.)

Nonresidential Fixed Investment Share of Real GDPVs. Capital Stock Growth

Nonres. Fixed Investment Share Capital Stock Growth20172013200920052001199719931989

900

800

700

600

500

400

300

2.5

2.0

1.5

1.0

0.5

0.0

(Bil. 2009 $) (Mil. Units)

Real Investment in Residential StructuresVs. New Housing Starts

Real Investment (Left) Housing Starts (Rt.)

2017201220072002199719921987198219771972

3.02.52.01.51.00.50.0

-0.5

(10-Yr. % Ch.)

Real Hourly Wage CompensationVs. Productivity in Nonfarm Sector

Real Wage Productivity 20172013200920052001199719931989

2

0

-2

-4

-6

-8

-10

(Percent of GDP)Federal Surplus or Deficit

CHARTS – FORECAST


20172013200920052001199719931989

6

5

4

3

2

1

0

-1

(Percent of GDP)

Consumer Price Index Inflation

201720122007200219971992198719821977

100

80

60

40

20

0

(2009$/barrel)

Real Refiner's Cost of Crude Oil

20172013200920052001199719931989

1.61.41.21.00.80.60.40.20.0

(Indexed: 2005 = 1.00)

Real and Nominal Exchange RateIndustrial Countries Trade Weighted Average

Nominal Exchange Rate Real Exchange Rate201720102003199619891982197519681961

15

11

7

2

-2

(Percent)Treasury Yields Vs. CPI Inflation

Inflation 30-Year Bonds 90-Day Bills

CHARTS – FORECAST


201720102003199619891982197519681961

109876543

9590858075706560

(%) (100% - Capacity Util.)

Unemployment and Capacity Utilization Mfg.Postwar Business Cycles

Unemployment Rate Capacity Util. Mfg. Rate 2017201420112008200520021999199619931990

12

11

10

9

8

7

(Percent of GDP)Federal Transfers to Persons

20172013200920052001199719931989

3.5

3.0

2.5

2.0

1.5

(Percent of GDP)

Federal Transfers to PersonsFor Health Insurance

201720132009200520011997199319891985

2.5

2.0

1.5

1.0

0.5

0.0

18161412108642

(Mil. Units) (Percent)

U.S. Housing StartsVs. Mortgage Rate

Housing Starts Mortgage Rate

CHARTS – FORECAST


20172013200920052001199719931989

20

15

10

5

0

(Mil. Units)

U.S. Retail Sales ofAutomobiles and Light Trucks

Automobiles Light Trucks 20172013200920052001199719931989

5.5

5.0

4.5

4.0

3.5

3.0

2.5

2.0

(Percent of National Income)

Federal Net Interest Payments onNational Debt

JUNE 2015 REPORT


Tables

FORECAST TABLES - SUMMARY


Table 1. Summary of the UCLA Anderson Forecast for the Nation 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Monetary Aggregates and GDP (% Ch.)Money Supply (M1) 0.2 -0.2 4.5 14.2 6.4 15.4 15.0 10.1 10.3 6.6 -3.4 -5.2Money Supply (M2) 5.3 6.2 6.8 8.1 2.5 7.3 8.6 6.7 6.2 5.7 3.1 2.4GDP Price Index 3.1 2.7 1.9 0.8 1.2 2.1 1.8 1.5 1.5 1.2 2.4 2.5Real GDP 2.7 1.8 -0.3 -2.8 2.5 1.6 2.3 2.2 2.4 2.4 3.0 2.6

Interest Rates (%) on:Federal Funds 5.0 5.0 1.9 0.2 0.2 0.1 0.1 0.1 0.1 0.3 1.2 2.990-day Treasury Bills 4.7 4.4 1.4 0.2 0.1 0.1 0.1 0.1 0.0 0.2 1.2 2.910-year Treasury Bonds 4.8 4.6 3.7 3.3 3.2 2.8 1.8 2.4 2.5 2.2 3.2 3.930-year Treasury Bonds 4.9 4.8 4.3 4.1 4.3 3.9 2.9 3.4 3.3 2.9 3.8 4.3Moody’s Corporate Aaa Bonds 5.6 5.6 5.6 5.3 4.9 4.6 3.7 4.2 4.2 3.8 4.8 5.530-yr Bond Less Inflation 2.2 2.3 1.2 4.1 2.6 1.5 1.1 2.2 2.0 2.6 1.6 1.7

Federal Fiscal PolicyDefense Purchases (% Ch.) Current $ 5.6 5.7 11.1 4.5 5.6 0.5 -2.3 -5.9 -1.1 0.7 3.5 3.8 Constant $ 2.0 2.5 7.5 5.4 3.2 -2.3 -3.3 -6.6 -2.1 0.7 1.7 1.5Other Expenditures (% Ch.) Transfers to Persons 6.6 6.4 8.8 17.1 7.0 -0.4 0.3 1.9 4.2 5.5 4.9 5.2 Grants to S&L Gov’t -0.7 5.3 3.4 23.5 10.3 -6.5 -5.9 1.3 11.3 6.7 6.4 4.8

Billions of Current Dollars, Unified Budget Basis, Fiscal YearReceipts 2406.7 2567.7 2523.6 2104.4 2161.7 2302.5 2449.1 2774.0 3020.4 3188.2 3445.7 3618.5Outlays 2654.9 2729.2 2978.4 3520.1 3455.9 3599.3 3538.3 3454.2 3503.7 3702.3 3865.3 4063.8Surplus or Deficit (-) -248.2 -161.5 -454.8 -1415.7 -1294.2 -1296.8 -1089.2 -680.2 -483.4 -514.1 -419.6 -445.3

As Shares of GDP (%), NIPA BasisRevenues 18.3 18.4 17.0 15.5 16.0 16.2 16.6 18.6 18.9 19.1 19.4 19.3Expenditures 19.9 20.2 21.3 24.1 24.9 24.3 23.3 22.4 22.3 22.1 22.0 21.9 Defense Purchases 4.6 4.7 5.1 5.5 5.6 5.4 5.1 4.6 4.4 4.2 4.2 4.1 Transfers to Persons 11.3 11.6 12.4 14.8 15.2 14.6 14.1 13.8 13.9 14.1 14.1 14.1Surplus or Deficit (-) -1.6 -1.8 -4.3 -8.7 -8.9 -8.0 -6.7 -3.9 -3.3 -3.0 -2.6 -2.7

Details of Real GDP (% Ch.)Real GDP 2.7 1.8 -0.3 -2.8 2.5 1.6 2.3 2.2 2.4 2.4 3.0 2.6Final Sales 2.6 2.0 0.2 -2.0 1.1 1.7 2.2 2.2 2.4 2.4 3.1 2.8Consumption 3.0 2.2 -0.3 -1.6 1.9 2.3 1.8 2.4 2.5 3.0 2.8 2.6Nonres. Fixed Investment 7.1 5.9 -0.7 -15.6 2.5 7.7 7.2 3.0 6.3 3.6 7.2 6.2 Equipment 8.6 3.2 -6.9 -22.9 15.9 13.6 6.8 4.6 6.4 5.7 8.7 5.6 Intellectual Property 4.5 4.8 3.0 -1.4 1.9 3.5 3.9 3.4 4.8 7.8 6.0 4.0 Structures 7.2 12.7 6.1 -18.9 -16.4 2.3 13.1 -0.5 8.2 -6.4 5.7 10.5Residential Construction -7.7 -19.0 -24.3 -21.4 -2.7 0.5 13.8 12.0 1.5 9.2 13.6 5.1Exports 9.0 9.3 5.7 -8.8 11.9 6.9 3.3 3.0 3.2 1.6 5.0 4.7Imports 6.3 2.5 -2.6 -13.7 12.7 5.5 2.3 1.1 4.0 5.0 7.0 5.0Federal Purchases 2.5 1.7 6.8 5.7 4.3 -2.7 -1.8 -5.7 -1.9 0.5 0.7 0.8State & Local Purchases 0.9 1.5 0.3 1.6 -2.7 -3.3 -1.2 0.5 1.0 1.1 1.2 0.9

Billions of 2009 DollarsReal GDP 14613.8 14873.8 14830.4 14418.8 14783.8 15020.6 15369.2 15710.3 16085.6 16475.2 16966.3 17414.6Final Sales 14542.2 14838.2 14864.1 14566.3 14725.6 14983.0 15312.1 15646.7 16015.1 16400.4 16905.4 17373.0Inventory Change 71.6 35.6 -33.7 -147.6 58.2 37.6 57.1 63.6 70.6 74.8 60.8 41.6



Table 2. Summary of the UCLA Anderson Forecast for the Nation 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Industrial Production and Resource UtilizationIndustrial Prod. (% Ch.) 2.2 2.5 -3.4 -11.3 5.7 3.3 3.8 2.9 4.2 1.7 3.4 3.4Capacity Util. Manuf. (%) 78.4 78.7 74.6 65.6 71.1 73.9 75.5 76.1 77.2 77.2 78.1 77.6Real Bus. Investment as % of Real GDP 18.2 17.5 16.4 14.0 13.9 14.6 15.4 15.8 16.2 16.6 17.5 18.1Nonfarm Employment (mil.) 136.4 137.9 137.2 131.2 130.3 131.8 134.1 136.4 139.0 141.9 144.1 146.1Unemployment Rate (%) 4.6 4.6 5.8 9.3 9.6 8.9 8.1 7.4 6.2 5.4 5.0 4.9

Inflation (% Ch.)Consumer Price Index 3.2 2.9 3.8 -0.3 1.6 3.1 2.1 1.5 1.6 0.0 2.6 3.1 Total less Food & Energy 2.5 2.3 2.3 1.7 1.0 1.7 2.1 1.8 1.7 1.8 2.4 2.6Consumption Chain Index 2.7 2.5 3.1 -0.1 1.7 2.5 1.8 1.2 1.3 0.3 2.2 2.5GDP Chain Index 3.1 2.7 1.9 0.8 1.2 2.1 1.8 1.5 1.5 1.2 2.4 2.5Producers Price Index 4.7 4.8 9.8 -8.7 6.8 8.8 0.5 0.6 1.0 -6.9 3.2 4.1

Factors Related to Inflation (% Ch.)Nonfarm Business Sector Wage Compensation 3.9 4.3 2.7 1.1 1.9 2.2 2.7 1.1 2.5 2.7 4.0 4.2 Productivity 0.9 1.6 0.8 3.2 3.3 0.2 1.0 0.9 0.7 0.3 2.0 1.8 Unit Labor Costs 3.0 2.7 2.0 -2.0 -1.3 2.1 1.7 0.3 1.8 2.4 1.9 2.4Farm Price Index -1.2 22.5 12.4 -16.5 12.2 23.6 3.2 1.4 1.1 -12.5 0.4 1.4Crude Oil Price ($/bbl) 66.1 72.3 99.6 61.7 79.4 95.1 94.2 98.0 93.0 53.7 67.6 82.1New Home Price ($1000) 243.1 243.7 230.4 214.5 221.2 224.3 242.1 265.1 283.8 295.7 294.5 303.5

Income, Consumption and Saving (% Ch.)Disposable Income 6.8 4.7 4.6 -0.5 2.7 5.0 4.9 1.0 3.8 3.7 4.6 6.1Real Disposable Income 4.0 2.1 1.5 -0.4 1.0 2.5 3.0 -0.2 2.5 3.4 2.3 3.4Real Consumption 3.0 2.2 -0.3 -1.6 1.9 2.3 1.8 2.4 2.5 3.0 2.8 2.6Savings Rate (%) 3.3 3.0 5.0 6.2 5.6 6.0 7.2 4.9 4.9 5.1 4.6 5.3

Housing and Automobiles--millions of unitsHousing Starts 1.812 1.342 0.900 0.554 0.586 0.612 0.784 0.930 1.001 1.164 1.370 1.422Auto & Light Truck Sales 16.5 16.1 13.2 10.4 11.6 12.7 14.4 15.5 16.4 16.9 17.2 17.5

Corporate ProfitsBillions of Dollars Before Taxes 1851.4 1748.4 1382.5 1472.6 1840.7 1806.8 2136.1 2235.3 2419.9 2722.8 2925.8 2861.3 After Taxes 1378.1 1302.9 1073.3 1203.1 1470.2 1427.7 1681.3 1761.1 1827.3 2091.1 2270.7 2242.2Percent Change Before Taxes 12.0 -5.6 -20.9 6.5 25.0 -1.8 18.2 4.6 8.3 12.5 7.5 -2.2 After Taxes 11.1 -5.5 -17.6 12.1 22.2 -2.9 17.8 4.7 3.8 14.4 8.6 -1.3

International Trade FactorsNominalU.S. Dollar--% change Industrial Countries -1.5 -5.6 -4.5 4.3 -3.0 -5.9 3.7 3.3 3.3 15.6 -0.3 -2.8 Developing Countries -2.5 -3.8 -2.6 7.2 -4.1 -3.5 2.0 -0.4 3.0 7.5 0.5 -0.7 Exports 12.8 12.8 10.7 -13.8 16.7 13.7 4.2 3.1 3.3 -2.1 7.6 7.2 Imports 10.7 6.0 7.6 -22.7 19.3 13.6 2.8 0.3 3.8 -2.9 7.5 7.7 Net Exports (bil. $) -771 -719 -723 -395 -513 -580 -568 -508 -538 -505 -541 -595RealU.S. Dollar--% change Industrial Countries -2.4 -6.4 -5.3 7.8 -0.5 -7.9 3.8 4.6 4.3 18.6 -0.5 -2.9 Developing Countries -5.1 -7.4 -9.5 6.3 -5.2 -8.2 -0.5 -1.2 2.2 8.2 -0.4 -2.1 Exports 9.0 9.3 5.7 -8.8 11.9 6.9 3.3 3.0 3.2 1.6 5.0 4.7 Imports 6.3 2.5 -2.6 -13.7 12.7 5.5 2.3 1.1 4.0 5.0 7.0 5.0 Net Exports (bil. ‘09$) -794 -713 -558 -395 -459 -459 -452 -420 -453 -547 -628 -665

FORECAST TABLES - QUARTERLY SUMMARY


Table 3. Quarterly Summary of the UCLA National Anderson Forecast for the Nation 2015:1 2015:2 2015:3 2015:4 2016:1 2016:2 2016:3 2016:4 2017:1 2017:2 2017:3

Monetary Aggregates and GDP (% Ch.)Money Supply (M1) 12.5 5.7 -1.0 -2.7 -4.5 -5.7 -5.1 -5.1 -5.1 -5.3 -5.7Money Supply (M2) 7.8 6.4 3.5 2.5 2.6 2.8 3.0 3.3 2.3 1.7 1.9GDP Price Index -0.1 2.4 2.2 1.9 2.5 2.6 2.7 2.8 2.5 2.3 2.5Real GDP 0.2 2.4 3.3 3.1 2.8 2.9 3.1 3.2 2.6 2.2 2.3

Interest Rates (%) on:Federal Funds 0.1 0.1 0.3 0.5 0.8 1.0 1.3 1.7 2.2 2.7 3.290-day Treasury Bills 0.0 0.0 0.3 0.5 0.8 1.0 1.3 1.7 2.2 2.6 3.210-year Treasury Bonds 2.0 2.1 2.3 2.6 2.8 3.1 3.3 3.5 3.8 4.0 4.030-year Treasury Bonds 2.5 2.8 3.0 3.3 3.5 3.7 3.9 4.0 4.2 4.4 4.3Moody’s Corporate Aaa Bonds 3.6 3.7 3.9 4.2 4.4 4.7 4.9 5.1 5.4 5.6 5.630-yr Bond Less Inflation 4.5 1.5 1.2 1.6 1.3 1.3 1.2 1.2 1.6 2.0 1.9

Federal Fiscal PolicyDefense Purchases (% Ch.) Current $ -1.7 3.7 6.9 2.5 3.2 3.4 2.4 3.8 5.7 3.0 3.0 Constant $ -0.7 3.6 5.9 1.1 -0.1 1.8 0.7 1.8 1.9 1.4 1.3Other Expenditures (% Ch.) Transfers to Persons 9.1 5.7 1.7 2.5 10.6 3.8 2.9 3.8 10.8 2.9 3.8 Grants to S&L Gov’t 13.5 5.4 2.3 2.9 15.8 3.4 3.8 4.2 7.4 3.7 3.9

Billions of Current Dollars, Unified Budget Basis, NSAReceipts 680.3 962.4 806.0 791.7 755.8 1034.7 863.5 832.8 804.6 1077.6 903.6Outlays 943.1 912.9 930.1 952.3 990.8 956.3 965.9 996.5 1040.3 1007.0 1020.0Surplus or Deficit (-) -262.8 49.4 -124.1 -160.6 -234.9 78.4 -102.4 -163.7 -235.7 70.6 -116.5

As Shares of GDP (%), NIPA BasisRevenues 19.1 19.0 19.1 19.2 19.4 19.5 19.4 19.3 19.5 19.4 19.2Expenditures 22.1 22.3 22.2 22.0 22.1 22.1 21.9 21.8 22.0 22.0 21.9 Defense Purchases 4.3 4.2 4.3 4.2 4.2 4.2 4.2 4.1 4.1 4.1 4.1 Transfers to Persons 14.2 14.2 14.1 14.0 14.2 14.1 14.0 14.0 14.1 14.1 14.0Surplus or Deficit (-) -3.0 -3.3 -3.1 -2.8 -2.7 -2.6 -2.5 -2.5 -2.5 -2.6 -2.7

Details of Real GDP (% Ch.)Real GDP 0.2 2.4 3.3 3.1 2.8 2.9 3.1 3.2 2.6 2.2 2.3Final Sales -0.5 3.5 3.5 3.1 2.9 2.9 3.1 3.1 2.8 2.4 2.4Consumption 1.9 3.0 3.3 3.1 2.6 2.6 2.9 2.8 2.6 2.2 2.7Nonres. Fixed Investment -3.4 2.7 6.6 9.2 8.1 7.1 6.0 6.9 7.1 5.6 4.6 Equipment 0.1 9.1 10.0 9.9 9.6 8.0 6.3 6.5 7.0 4.3 3.4 Intellectual Property 7.8 5.7 7.3 7.4 6.4 5.1 4.7 4.1 4.4 3.5 3.1 Structures -23.1 -13.9 -1.7 10.4 7.4 8.0 7.2 12.4 11.0 11.8 9.6Residential Construction 1.3 18.7 24.3 13.8 12.5 10.4 10.4 9.9 7.2 1.5 -2.1Exports -7.2 3.4 4.4 4.5 5.5 5.2 5.4 5.2 4.6 4.2 4.2Imports 1.8 2.5 8.7 9.2 7.3 6.7 4.8 5.6 5.8 3.9 3.9Federal Purchases 0.3 1.9 3.1 0.5 -0.6 1.0 0.2 1.0 1.1 0.7 0.6State & Local Purchases -1.5 2.7 1.9 1.3 1.1 0.7 0.5 0.8 1.0 1.1 1.1

Billions of 2009 DollarsReal GDP 16304.8 16400.3 16534.8 16660.9 16777.1 16899.2 17026.7 17162.2 17270.9 17364.8 17463.0Final Sales 16194.5 16332.7 16474.6 16599.9 16718.4 16836.9 16967.4 17099.0 17219.1 17321.0 17425.6Inventory Change 110.3 67.7 60.2 61.0 58.7 62.3 59.3 63.1 51.8 43.8 37.3

FORECAST TABLES - QUARTERLY SUMMARY


Table 4. Quarterly Summary of The UCLA National Anderson Forecast for the Nation 2015:1 2015:2 2015:3 2015:4 2016:1 2016:2 2016:3 2016:4 2017:1 2017:2 2017:3

Industrial Production and Resource UtilizationProduction--% change -1.0 -2.0 3.0 3.5 3.8 3.7 4.7 4.6 3.4 2.0 2.4Capacity Util. Manuf. (%) 77.2 77.0 77.2 77.5 77.7 78.1 78.3 78.4 78.1 77.7 77.4Real Bus. Investment as % of Real GDP 16.3 16.4 16.7 17.0 17.2 17.4 17.6 17.8 18.0 18.1 18.1Nonfarm Employment (mil.) 141.0 141.6 142.3 142.8 143.3 143.9 144.4 144.9 145.4 145.9 146.3Unemployment Rate (%) 5.6 5.4 5.3 5.2 5.1 5.0 4.9 4.8 4.8 4.8 4.9

Inflation--% changeConsumer Price Index -3.1 1.4 2.2 2.0 2.8 2.9 3.2 3.2 3.3 2.8 2.9 Total less Food & Energy 1.7 1.7 2.4 2.4 2.4 2.5 2.6 2.7 2.6 2.6 2.6Consumption Deflator -2.0 1.3 1.9 1.7 2.2 2.5 2.7 2.8 2.6 2.3 2.4GDP Deflator -0.1 2.4 2.2 1.9 2.5 2.6 2.7 2.8 2.5 2.3 2.5Producers Price Index -19.1 -4.7 3.0 2.4 3.8 4.8 5.0 3.9 4.9 3.0 4.0

Factors Related to Inflation--%changeNonfarm Business Sector Wage Compensation 3.1 3.1 3.9 4.1 3.9 3.9 4.3 4.4 4.4 3.9 4.3 Productivity -1.9 0.7 2.3 2.4 2.0 2.0 2.1 2.2 1.6 1.5 1.8 Unit Labor Costs 5.0 2.4 1.5 1.6 1.9 1.9 2.2 2.1 2.7 2.3 2.5Farm Price Index -30.9 -16.5 1.3 1.7 1.7 1.7 1.7 1.1 1.4 1.4 1.5Crude Oil Price ($/bbl) 48.7 51.0 55.6 59.5 61.9 65.8 68.9 73.8 79.5 81.4 83.1New Home Price ($1000) 283.8 301.5 299.3 298.3 289.1 295.6 296.5 296.7 301.8 306.1 303.0

Income, Consumption and Saving--%changeDisposable Income 4.1 3.5 3.5 3.7 5.1 4.6 5.6 6.2 6.4 6.1 6.3Real Disposable Income 6.2 2.2 1.6 1.9 2.8 2.1 2.9 3.3 3.7 3.6 3.8Real Consumption 1.9 3.0 3.3 3.1 2.6 2.6 2.9 2.8 2.6 2.2 2.7Savings Rate (%) 5.5 5.3 4.9 4.6 4.7 4.5 4.5 4.7 4.9 5.2 5.5

Housing and Automobiles--millions of unitsHousing Starts 0.969 1.162 1.244 1.283 1.314 1.347 1.388 1.432 1.464 1.457 1.399Auto and Light Truck Sales 16.6 16.9 17.0 17.1 17.1 17.1 17.2 17.3 17.5 17.4 17.5

Corporate ProfitsBillions of Dollars Before Taxes 2550.6 2678.0 2805.2 2857.5 2853.7 2934.7 2946.7 2968.0 2908.3 2877.4 2855.7 After Taxes 1923.1 2064.1 2166.6 2210.5 2200.0 2272.2 2289.7 2321.1 2269.2 2253.5 2242.8Percent Change Before Taxes 20.3 21.5 20.4 7.7 -0.5 11.9 1.6 2.9 -7.8 -4.2 -3.0 After Taxes 20.0 32.7 21.4 8.4 -1.9 13.8 3.1 5.6 -8.6 -2.7 -1.9

International TradeNominalU.S. Dollar--% change Industrial Countries 37.2 4.6 3.0 2.9 -2.9 -2.6 -1.3 -2.2 -2.1 -3.6 -4.6 Developing Countries 17.7 2.1 1.7 0.4 -0.1 -0.1 0.6 1.1 -1.4 -3.1 -0.0 Exports--% change -16.5 2.6 6.7 6.7 8.5 8.7 8.4 8.2 7.0 6.2 6.4 Imports--% change -14.9 -7.0 7.8 10.3 8.5 8.5 6.9 7.9 9.4 6.9 6.4 Net Exports (bil. $) -537.9 -473.8 -488.7 -520.3 -531.2 -541.5 -541.8 -550.6 -577.1 -590.9 -600.1RealU.S. Dollar--% change Industrial Countries 42.9 8.1 6.2 3.8 -4.6 -4.3 -2.5 -2.5 -1.7 -3.2 -4.3 Developing Countries 19.3 2.8 2.4 0.1 -1.6 -1.6 -1.2 -0.6 -2.6 -4.1 -1.0 Exports--% change -7.2 3.4 4.4 4.5 5.5 5.2 5.4 5.2 4.6 4.2 4.2 Imports--% change 1.8 2.5 8.7 9.2 7.3 6.7 4.8 5.6 5.8 3.9 3.9 Net Exports (bil. ‘09$) -522.1 -521.3 -553.8 -590.3 -609.9 -627.6 -632.2 -643.1 -658.7 -663.1 -667.7

FORECAST TABLES - DETAILED


Table 5. Part A. Gross Domestic Product 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Billions of Current DollarsGross Domestic Product 13855.9 14477.6 14718.6 14418.7 14964.4 15517.9 16163.2 16768.1 17418.9 18050.3 19036.7 20034.6Personal ConsumptionExpenditures 9304.0 9750.5 10013.6 9847.0 10202.2 10689.3 11083.1 11484.3 11930.3 12331.0 12956.8 13633.2 Durable Goods 1156.1 1184.6 1102.3 1023.3 1070.7 1125.3 1192.1 1249.3 1302.5 1357.4 1421.1 1495.3 Autos and Parts 394.9 400.6 339.6 317.1 342.0 363.5 395.1 417.7 447.8 481.0 513.7 550.0 Nondurable Goods 2079.7 2176.9 2273.4 2175.1 2292.1 2471.1 2549.8 2601.9 2666.2 2644.2 2792.1 2947.3 Services 6068.2 6388.9 6637.9 6648.5 6839.4 7092.8 7341.3 7633.2 7961.7 8329.4 8743.6 9190.7Gross Private DomesticInvestment 2680.7 2643.7 2424.8 1878.1 2100.8 2239.9 2479.2 2648.0 2851.6 3011.9 3302.1 3554.1 Residential 837.4 688.7 515.9 392.3 381.1 386.0 442.3 519.9 559.1 628.7 732.1 794.9 Nonres. Structures 415.6 496.9 552.4 438.2 362.0 381.6 446.9 457.2 506.9 482.7 526.9 600.9 Equipment 856.1 885.8 825.1 644.3 731.8 838.2 904.1 949.7 1017.3 1080.6 1184.2 1271.4 Intellectual Property 504.6 538.0 563.4 550.9 564.4 592.2 621.0 647.2 686.3 737.8 790.6 838.9 Change In Inv. 67.0 34.5 -32.0 -147.6 61.5 41.8 64.9 74.1 82.1 82.1 68.2 48.0

Net Exports -771.0 -718.6 -723.1 -395.5 -512.7 -580.0 -568.3 -508.2 -538.2 -505.1 -541.3 -595.1Exports 1476.3 1664.6 1841.9 1587.7 1852.3 2106.4 2194.2 2262.2 2337.0 2287.4 2460.2 2638.3Imports 2247.3 2383.2 2565.0 1983.2 2365.0 2686.4 2762.5 2770.4 2875.2 2792.5 3001.4 3233.4

Government Purchases 2642.2 2801.9 3003.2 3089.1 3174.0 3168.7 3169.2 3143.9 3175.2 3212.5 3319.1 3442.3 Federal 1002.0 1049.8 1155.6 1217.7 1303.9 1303.5 1291.4 1231.5 1219.2 1228.7 1258.7 1295.6 Defense 642.4 678.7 754.1 788.3 832.8 837.0 818.0 769.9 761.5 767.0 794.0 824.0 Other 359.6 371.1 401.5 429.4 471.1 466.5 473.4 461.6 457.7 461.7 464.7 471.6 State and Local 1640.2 1752.2 1847.6 1871.4 1870.2 1865.3 1877.8 1912.4 1956.1 1983.8 2060.4 2146.7

Billions of 2009 DollarsGross Domestic Product 14613.8 14873.8 14830.4 14418.8 14783.8 15020.6 15369.2 15710.3 16085.6 16475.2 16966.3 17414.6Personal ConsumptionExpenditures 9821.7 10041.6 10007.2 9847.0 10036.3 10263.5 10449.7 10699.7 10969.0 11303.1 11624.9 11929.1 Durable Goods 1091.5 1141.7 1083.2 1023.3 1085.7 1151.5 1235.7 1319.0 1410.0 1503.4 1588.9 1682.9 Autos & Parts 385.1 392.8 340.8 317.1 323.4 333.8 357.9 376.0 405.0 434.6 458.8 484.9 Nondurable Goods 2202.2 2239.3 2214.7 2175.1 2223.5 2263.2 2280.1 2322.6 2364.8 2418.2 2480.7 2537.8 Services 6526.6 6656.4 6708.6 6648.5 6727.6 6851.4 6942.4 7073.1 7218.6 7415.6 7599.1 7765.6Gross Private DomesticInvestment 2730.0 2644.1 2396.0 1878.1 2120.4 2230.4 2435.9 2556.2 2704.7 2831.5 3053.1 3209.3 Residential 806.6 654.8 497.7 392.3 382.4 384.5 436.5 488.4 496.2 541.4 614.4 645.7 Nonres. Structures 451.5 509.0 540.2 438.2 366.3 374.7 423.8 421.7 456.2 427.3 451.7 499.2 Equipment 870.8 898.3 836.1 644.3 746.7 847.9 905.6 947.2 1008.2 1065.4 1158.6 1223.6 Intellectual Property 517.5 542.4 558.8 550.9 561.3 581.3 603.8 624.1 654.2 704.9 747.5 777.7 Change In Inv. 71.6 35.6 -33.7 -147.6 58.2 37.6 57.1 63.6 70.6 74.8 60.8 41.6

Net Exports -794.3 -712.6 -557.8 -395.4 -458.8 -459.4 -452.5 -420.5 -452.6 -546.8 -628.2 -665.2Exports 1506.8 1646.4 1740.8 1587.7 1776.6 1898.3 1960.1 2019.8 2084.7 2118.2 2223.2 2328.3Imports 2301.0 2359.0 2298.6 1983.2 2235.4 2357.7 2412.6 2440.3 2537.3 2665.1 2851.5 2993.5

Government Purchases 2869.3 2914.4 2994.8 3089.1 3091.4 2997.4 2954.0 2894.5 2889.7 2915.1 2944.3 2969.4 Federal 1060.9 1078.7 1152.3 1217.7 1270.7 1236.4 1214.4 1145.3 1123.5 1129.6 1137.5 1146.4 Defense 678.8 695.6 748.1 788.3 813.5 795.0 768.7 717.7 702.4 707.6 719.5 730.5 Other 382.1 383.1 404.2 429.4 457.1 441.4 445.7 427.5 421.0 421.8 417.9 415.8 State and Local 1808.9 1836.2 1842.5 1871.4 1820.8 1761.0 1739.5 1748.5 1765.3 1784.4 1805.5 1821.8



Table 5. Part B. Gross Domestic Product 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Annual Rates of Change of Current Dollar GDP Components (%)Gross Domestic Product 5.8 4.5 1.7 -2.0 3.8 3.7 4.2 3.7 3.9 3.6 5.5 5.2Personal ConsumptionExpenditures 5.8 4.8 2.7 -1.7 3.6 4.8 3.7 3.6 3.9 3.4 5.1 5.2 Durable Goods 2.6 2.5 -7.0 -7.2 4.6 5.1 5.9 4.8 4.3 4.2 4.7 5.2 Autos and Parts -3.7 1.4 -15.2 -6.6 7.9 6.3 8.7 5.7 7.2 7.4 6.8 7.1 Nondurable Goods 6.5 4.7 4.4 -4.3 5.4 7.8 3.2 2.0 2.5 -0.8 5.6 5.6 Services 6.2 5.3 3.9 0.2 2.9 3.7 3.5 4.0 4.3 4.6 5.0 5.1Gross Private DomesticInvestment 6.1 -1.4 -8.3 -22.5 11.9 6.6 10.7 6.8 7.7 5.6 9.6 7.6 Residential -2.2 -17.8 -25.1 -24.0 -2.9 1.3 14.6 17.5 7.5 12.5 16.5 8.6 Nonres. Structures 20.2 19.6 11.2 -20.7 -17.4 5.4 17.1 2.3 10.9 -4.8 9.2 14.0 Equipment 8.3 3.5 -6.8 -21.9 13.6 14.5 7.9 5.1 7.1 6.2 9.6 7.4 Intellectual Property 6.2 6.6 4.7 -2.2 2.5 4.9 4.9 4.2 6.0 7.5 7.2 6.1

Exports 12.8 12.8 10.7 -13.8 16.7 13.7 4.2 3.1 3.3 -2.1 7.6 7.2Imports 10.7 6.0 7.6 -22.7 19.3 13.6 2.8 0.3 3.8 -2.9 7.5 7.7

Government Purchases 6.0 6.0 7.2 2.9 2.7 -0.2 0.0 -0.8 1.0 1.2 3.3 3.7 Federal 5.9 4.8 10.1 5.4 7.1 -0.0 -0.9 -4.6 -1.0 0.8 2.4 2.9 Defense 5.6 5.7 11.1 4.5 5.6 0.5 -2.3 -5.9 -1.1 0.7 3.5 3.8 Other 6.4 3.2 8.2 7.0 9.7 -1.0 1.5 -2.5 -0.9 0.9 0.7 1.5 State and Local 6.0 6.8 5.4 1.3 -0.1 -0.3 0.7 1.8 2.3 1.4 3.9 4.2

Annual Rates of Change of Constant Dollar GDP Components (%)Gross Domestic Product 2.7 1.8 -0.3 -2.8 2.5 1.6 2.3 2.2 2.4 2.4 3.0 2.6Personal ConsumptionExpenditures 3.0 2.2 -0.3 -1.6 1.9 2.3 1.8 2.4 2.5 3.0 2.8 2.6 Durable Goods 4.3 4.6 -5.1 -5.5 6.1 6.1 7.3 6.7 6.9 6.6 5.7 5.9 Autos & Parts -3.7 2.0 -13.2 -7.0 2.0 3.2 7.2 5.1 7.7 7.3 5.6 5.7 Nondurable Goods 3.3 1.7 -1.1 -1.8 2.2 1.8 0.7 1.9 1.8 2.3 2.6 2.3 Services 2.7 2.0 0.8 -0.9 1.2 1.8 1.3 1.9 2.1 2.7 2.5 2.2Gross Private DomesticInvestment 2.1 -3.1 -9.4 -21.6 12.9 5.2 9.2 4.9 5.8 4.7 7.8 5.1 Residential -7.6 -18.8 -24.0 -21.2 -2.5 0.5 13.5 11.9 1.6 9.1 13.5 5.1 Nonres. Structures 7.2 12.7 6.1 -18.9 -16.4 2.3 13.1 -0.5 8.2 -6.4 5.7 10.5 Equipment 8.6 3.2 -6.9 -22.9 15.9 13.6 6.8 4.6 6.4 5.7 8.7 5.6 Intellectual Property 4.5 4.8 3.0 -1.4 1.9 3.5 3.9 3.4 4.8 7.8 6.0 4.0

Exports 9.0 9.3 5.7 -8.8 11.9 6.9 3.3 3.0 3.2 1.6 5.0 4.7Imports 6.3 2.5 -2.6 -13.7 12.7 5.5 2.3 1.1 4.0 5.0 7.0 5.0

Government Purchases 1.5 1.6 2.8 3.1 0.1 -3.0 -1.4 -2.0 -0.2 0.9 1.0 0.9 Federal 2.5 1.7 6.8 5.7 4.3 -2.7 -1.8 -5.7 -1.9 0.5 0.7 0.8 Defense 2.0 2.5 7.5 5.4 3.2 -2.3 -3.3 -6.6 -2.1 0.7 1.7 1.5 Other 3.5 0.3 5.5 6.2 6.5 -3.4 1.0 -4.1 -1.5 0.2 -0.9 -0.5 State and Local 0.9 1.5 0.3 1.6 -2.7 -3.3 -1.2 0.5 1.0 1.1 1.2 0.9



Table 6. Employment 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Employment (Millions)Total 144.4 146.1 145.4 139.9 139.1 139.9 142.5 143.9 146.3 149.3 152.0 154.3 Nonagricultural 136.4 137.9 137.2 131.2 130.3 131.8 134.1 136.4 139.0 141.9 144.1 146.1 Natural Res. & Mining 0.7 0.7 0.8 0.7 0.7 0.8 0.8 0.9 0.9 0.8 0.8 0.8 Construction 7.7 7.6 7.2 6.0 5.5 5.5 5.6 5.9 6.1 6.5 6.9 7.4 Manufacturing 14.2 13.9 13.4 11.8 11.5 11.7 11.9 12.0 12.2 12.4 12.5 12.5 Trans. Warehous. Util 5.0 5.1 5.1 4.8 4.7 4.9 5.0 5.0 5.2 5.4 5.5 5.6 Trade 21.3 21.5 21.2 20.1 19.9 20.2 20.5 20.8 21.2 21.6 21.7 21.7 Financial Activities 8.4 8.3 8.2 7.8 7.7 7.7 7.8 7.9 8.0 8.1 8.0 7.9 Information 3.0 3.0 3.0 2.8 2.7 2.7 2.7 2.7 2.7 2.8 2.8 2.9 Professional & Busi. 17.6 17.9 17.7 16.6 16.7 17.3 17.9 18.5 19.1 19.8 21.0 21.7 Education & Health 18.1 18.6 19.2 19.5 19.9 20.2 20.7 21.1 21.5 22.0 22.3 22.6 Leisure & Hospitality 13.1 13.4 13.4 13.1 13.0 13.4 13.8 14.3 14.7 15.1 15.1 15.2 Other Services 5.4 5.5 5.5 5.4 5.3 5.4 5.4 5.5 5.6 5.6 5.6 5.5 Government 22.0 22.2 22.5 22.6 22.5 22.1 21.9 21.8 21.9 21.9 21.9 22.1 Federal 2.7 2.7 2.8 2.8 3.0 2.9 2.8 2.8 2.7 2.7 2.7 2.7 State & Local 19.2 19.5 19.7 19.7 19.5 19.2 19.1 19.1 19.1 19.2 19.2 19.4

Population and Labor Force (Millions)Population aged 16+ 234.2 237.0 239.6 242.2 244.6 247.0 249.2 251.5 253.8 256.4 259.0 261.6Labor Force 151.4 153.1 154.3 154.2 153.9 153.6 155.0 155.4 155.9 157.8 160.0 162.3Unemployment (%) 4.6 4.6 5.8 9.3 9.6 8.9 8.1 7.4 6.2 5.4 5.0 4.9

Table 7. Personal Income and Its Disposition 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Billions of Current DollarsPersonal Income 11389.0 11994.9 12429.6 12087.5 12429.4 13202.0 13887.7 14166.9 14728.6 15328.6 16102.3 17086.6Wages & Salaries 6057.4 6395.2 6531.9 6251.4 6377.5 6633.2 6932.1 7124.7 7446.0 7813.5 8242.9 8698.7Other Labor Income 997.6 1041.4 1075.1 1077.5 1114.6 1142.0 1160.5 1193.9 1226.4 1265.5 1315.0 1378.1Nonfarm Income 1017.7 941.1 979.5 937.6 986.7 1068.1 1187.9 1253.5 1316.6 1374.2 1463.9 1532.2Farm Income 36.0 38.1 47.0 35.5 46.0 75.6 72.3 83.2 63.6 48.4 45.9 47.7Rental Income 207.5 189.4 262.1 333.7 402.8 485.3 533.0 595.8 640.3 665.4 662.1 669.9Dividends 723.7 816.6 805.5 553.8 544.6 682.3 832.7 824.6 860.6 905.4 954.5 1022.6Interest Income 1214.8 1350.1 1361.6 1264.3 1195.1 1231.6 1255.9 1255.2 1264.7 1244.7 1322.1 1539.7Transfer Payments 1609.7 1722.8 1884.0 2140.2 2276.9 2307.9 2350.7 2414.6 2522.7 2651.8 2778.7 2926.4Personal Contributions For Social Insurance 475.2 499.7 516.9 506.3 514.7 423.9 437.3 578.4 612.2 640.2 682.9 728.7

Personal Tax and Nontax Payments 1352.1 1487.9 1435.2 1144.9 1191.5 1400.6 1503.7 1661.8 1742.9 1858.9 2018.3 2149.5Disposable Income 10036.9 10507.0 10994.4 10942.5 11237.9 11801.4 12384.0 12505.2 12985.8 13469.7 14083.9 14937.1Consumption 9304.0 9750.5 10013.6 9847.0 10202.2 10689.3 11083.1 11484.3 11930.3 12331.0 12956.8 13633.2Interest 275.1 305.9 289.6 274.0 250.8 241.4 241.6 247.1 256.8 277.3 284.9 295.8Transfers To Foreigners 51.6 59.3 66.2 66.1 73.0 74.1 73.2 74.3 75.6 79.6 84.1 89.6Personal Saving 329.5 310.3 542.2 672.0 628.1 711.1 896.2 608.1 628.3 681.2 648.1 798.3

Personal Saving Rate(%) 3.3 3.0 5.0 6.2 5.6 6.0 7.2 4.9 4.9 5.1 4.6 5.3



Table 8. Personal Consumption Expenditures By Major Types 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Billions of Current DollarsPersonal Consumption 9304.0 9750.5 10013.6 9847.0 10202.2 10689.3 11083.1 11484.3 11930.3 12331.0 12956.8 13633.2 Durable Goods 1156.1 1184.6 1102.3 1023.3 1070.7 1125.3 1192.1 1249.3 1302.5 1357.4 1421.1 1495.3 Autos and Parts 394.9 400.6 339.6 317.1 342.0 363.5 395.1 417.7 447.8 481.0 513.7 550.0 Nondurable Goods 2079.7 2176.9 2273.4 2175.1 2292.1 2471.1 2549.8 2601.9 2666.2 2644.2 2792.1 2947.3 Services 6068.2 6388.9 6637.9 6648.5 6839.4 7092.8 7341.3 7633.2 7961.7 8329.4 8743.6 9190.7

Billions of 2009 DollarsPersonal Consumption 9821.7 10041.6 10007.2 9847.0 10036.3 10263.5 10449.7 10699.7 10969.0 11303.1 11624.9 11929.1 Durable Goods 1091.5 1141.7 1083.2 1023.3 1085.7 1151.5 1235.7 1319.0 1410.0 1503.4 1588.9 1682.9 Autos and Parts 385.1 392.8 340.8 317.1 323.4 333.8 357.9 376.0 405.0 434.6 458.8 484.9 Nondurable Goods 2202.2 2239.3 2214.7 2175.1 2223.5 2263.2 2280.1 2322.6 2364.8 2418.2 2480.7 2537.8 Services 6526.6 6656.4 6708.6 6648.5 6727.6 6851.4 6942.4 7073.1 7218.6 7415.6 7599.1 7765.6

Annual Rates of Real GrowthPersonal Consumption 3.0 2.2 -0.3 -1.6 1.9 2.3 1.8 2.4 2.5 3.0 2.8 2.6 Durable Goods 4.3 4.6 -5.1 -5.5 6.1 6.1 7.3 6.7 6.9 6.6 5.7 5.9 Autos and Parts -3.7 2.0 -13.2 -7.0 2.0 3.2 7.2 5.1 7.7 7.3 5.6 5.7 Furniture 5.1 0.8 -4.6 -8.7 7.0 5.8 4.3 5.8 6.1 5.2 4.1 3.5 Other Durables 7.2 4.7 -3.3 -5.0 4.2 5.5 5.5 5.9 3.4 3.3 2.2 2.3 Nondurable Goods 3.3 1.7 -1.1 -1.8 2.2 1.8 0.7 1.9 1.8 2.3 2.6 2.3 Food and Beverages 3.1 1.3 -1.2 -1.5 2.1 1.1 0.8 1.0 0.0 0.0 2.6 2.5 Gasoline and Oil 0.4 -0.3 -3.9 -0.8 -0.1 -2.0 -1.3 1.0 0.9 3.7 0.7 -0.6 Fuel -6.6 1.1 -11.3 15.0 -7.9 -12.4 -9.1 0.1 2.9 6.6 -3.2 -2.2 Clothing and Shoes 3.5 2.0 -0.5 -4.9 5.3 3.9 0.7 1.0 0.9 2.7 2.6 2.3 Other Nondurables 4.9 2.7 0.4 -1.7 2.3 3.6 1.9 3.4 4.1 3.5 3.3 3.1 Services 2.7 2.0 0.8 -0.9 1.2 1.8 1.3 1.9 2.1 2.7 2.5 2.2 Housing 2.7 0.9 1.5 1.3 1.1 1.8 1.2 1.2 0.9 1.0 1.4 1.7 Transportation Serv. 0.2 1.0 -5.2 -9.8 -0.9 2.4 1.9 2.7 2.5 3.5 3.6 2.8 Health Care 2.3 2.5 2.3 1.8 1.3 2.5 3.2 2.1 2.8 4.9 3.6 3.3 Recreational Service 3.5 3.9 -0.8 -3.3 1.3 2.3 1.7 2.4 0.1 1.5 1.6 3.1 Food Svcs. Accom. 3.2 1.3 -1.0 -4.1 1.5 2.6 2.5 2.2 3.0 4.2 3.4 2.8 Financial Services 2.3 3.1 -0.7 -2.5 2.1 1.8 -4.5 2.1 4.1 3.1 2.6 1.3 Other Services 2.6 2.3 -0.7 -2.2 0.2 1.3 2.1 0.6 0.4 0.9 2.6 1.7

Table 9. Residential Construction and Housing Starts 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Housing Starts (Millions of Units)Housing Starts 1.812 1.342 0.900 0.554 0.586 0.612 0.784 0.930 1.001 1.164 1.370 1.422 Single-family 1.474 1.036 0.616 0.442 0.471 0.434 0.537 0.621 0.646 0.764 0.928 0.997 Multi-family 0.338 0.306 0.284 0.112 0.114 0.178 0.247 0.309 0.355 0.401 0.443 0.426

Residential Construction Expenditures (Billions of Dollars)Current Dollars 837.4 688.7 515.9 392.3 381.1 386.0 442.3 519.9 559.1 628.7 732.1 794.92009 Dollars 806.6 654.8 497.7 392.3 382.4 384.5 436.5 488.4 496.2 541.4 614.4 645.7 % Change -7.6 -18.8 -24.0 -21.2 -2.5 0.5 13.5 11.9 1.6 9.1 13.5 5.1

Related ConceptsTreas. Bill Rate 4.73 4.35 1.37 0.15 0.14 0.05 0.09 0.06 0.03 0.21 1.20 2.87Conventional 30-year Mortgage Rate 6.41 6.34 6.04 5.04 4.69 4.46 3.66 3.98 4.17 3.92 4.93 5.83Median Sales Price of New Homes (Thous $) 243.1 243.7 230.4 214.5 221.2 224.3 242.1 265.1 283.8 295.7 294.5 303.5Real Disp. Income 10036.9 10507.0 10994.4 10942.5 11237.9 11801.4 12384.0 12505.2 12985.8 13469.7 14083.9 14937.1 % Change 4.0 2.1 1.5 -0.4 1.0 2.5 3.0 -0.2 2.5 3.4 2.3 3.4



Table 10. Nonresidential Fixed Investment and Inventories 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Billions of Current DollarsNonres. Fixed Investment 1776.3 1920.6 1941.0 1633.4 1658.2 1812.1 1972.0 2054.0 2210.5 2301.1 2501.8 2711.2 Equipment 856.1 885.8 825.1 644.3 731.8 838.2 904.1 949.7 1017.3 1080.6 1184.2 1271.4 Intellectual Property 504.6 538.0 563.4 550.9 564.4 592.2 621.0 647.2 686.3 737.8 790.6 838.9 Nonresidential Structures 415.6 496.9 552.4 438.2 362.0 381.6 446.9 457.2 506.9 482.7 526.9 600.9 Buildings 244.8 293.9 317.5 249.1 173.7 170.2 191.6 201.7 224.0 247.8 283.7 327.1 Commercial 128.4 150.7 148.9 95.4 64.7 66.8 75.6 83.4 96.7 105.6 129.1 156.3 Industrial 32.3 40.2 52.8 56.3 39.8 39.0 45.8 46.3 53.8 67.5 65.2 62.0 Other Buildings 84.2 103.0 115.8 97.4 69.2 64.5 70.2 72.0 73.5 74.8 89.4 108.8 Utilities 63.6 89.6 104.6 104.3 93.3 90.7 112.2 105.6 119.0 108.9 112.1 122.7 Mining Exploration 96.0 102.2 117.0 75.0 86.2 112.3 133.1 139.7 153.9 115.5 119.4 136.2 Other 11.1 11.2 13.3 9.9 8.9 8.4 10.1 10.2 10.1 10.5 11.7 14.9

Billions of 2009 DollarsNonres. Fixed Investment 1839.6 1948.4 1934.5 1633.5 1673.8 1802.3 1931.8 1990.6 2116.4 2191.6 2350.4 2495.0 Equipment 870.8 898.3 836.1 644.3 746.7 847.9 905.6 947.2 1008.2 1065.4 1158.6 1223.6 Intellectual Property 517.5 542.4 558.8 550.9 561.3 581.3 603.8 624.1 654.2 704.9 747.5 777.7 Nonresidential Structures 451.5 509.0 540.2 438.2 366.3 374.7 423.8 421.7 456.2 427.3 451.7 499.2 Buildings 268.7 305.2 317.9 249.1 179.3 172.3 188.8 194.0 208.5 224.6 248.7 276.7 Commercial 144.3 159.9 151.7 95.4 66.6 67.3 73.9 79.8 90.1 96.3 115.2 135.4 Industrial 36.5 43.1 53.8 56.3 40.8 39.1 44.9 44.3 49.9 60.3 54.8 49.1 Other Buildings 88.5 102.6 112.8 97.4 71.9 65.9 70.0 69.8 68.3 67.8 79.2 93.5 Utilities 70.0 94.3 103.6 104.3 89.8 82.8 99.1 92.2 102.3 91.8 90.9 95.0 Mining Exploration 99.5 97.9 105.0 75.0 87.8 110.9 124.5 125.2 135.5 102.4 104.2 118.5 Other 10.8 10.6 12.6 9.9 9.2 8.6 10.1 9.9 9.3 9.1 9.1 10.7

Percent Change in Real Nonresidential Fixed InvestmentNonres. Fixed Investment 7.1 5.9 -0.7 -15.6 2.5 7.7 7.2 3.0 6.3 3.6 7.2 6.2 Equipment 8.6 3.2 -6.9 -22.9 15.9 13.6 6.8 4.6 6.4 5.7 8.7 5.6 Intellectual Property 4.5 4.8 3.0 -1.4 1.9 3.5 3.9 3.4 4.8 7.8 6.0 4.0 Nonresidential Structures 7.2 12.7 6.1 -18.9 -16.4 2.3 13.1 -0.5 8.2 -6.4 5.7 10.5 Buildings 7.2 13.6 4.2 -21.7 -28.0 -3.9 9.6 2.7 7.5 7.7 10.8 11.2 Commercial 4.9 10.8 -5.2 -37.1 -30.2 0.9 9.8 8.0 13.0 6.8 19.6 17.6 Industrial 6.6 18.2 24.8 4.6 -27.5 -4.2 14.8 -1.3 12.7 20.9 -9.2 -10.4 Other Buildings 11.0 16.0 9.9 -13.7 -26.2 -8.3 6.2 -0.4 -2.2 -0.7 16.8 18.1 Utilities 7.9 34.6 9.9 0.7 -13.9 -7.8 19.8 -7.0 11.0 -10.2 -1.0 4.5 Mining Exploration 8.0 -1.6 7.3 -28.6 17.1 26.4 12.3 0.5 8.2 -24.4 1.8 13.7 Other 0.8 -1.4 18.0 -21.3 -7.4 -5.9 17.5 -2.3 -6.2 -2.4 0.3 18.4

Related ConceptsAnnual Growth-Price Deflator For: Producers Dur. Equip. -0.3 0.3 0.1 1.3 -2.0 0.9 1.0 0.4 0.6 0.5 0.8 1.7 Structures 12.2 6.1 4.8 -2.2 -1.2 3.0 3.5 2.8 2.5 1.7 3.2 3.2Moody’s AAA Rate(%) 5.6 5.6 5.6 5.3 4.9 4.6 3.7 4.2 4.2 3.8 4.8 5.5Capacity Utilization in Manufacturing(%) 78.4 78.7 74.6 65.6 71.1 73.9 75.5 76.1 77.2 77.2 78.1 77.6Final Sales(Bil. 2009 $) 14542.2 14838.2 14864.1 14566.3 14725.6 14983.0 15312.1 15646.7 16015.1 16400.4 16905.4 17373.0

Change in Business InventoriesCurrent Dollars 67.0 34.5 -32.0 -147.6 61.5 41.8 64.9 74.1 82.1 82.1 68.2 48.02009 Dollars 71.6 35.6 -33.7 -147.6 58.2 37.6 57.1 63.6 70.6 74.8 60.8 41.6



Table 11. Federal Government Receipts and Expenditures 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Billions of Current Dollars Unified Budget Basis, Fiscal YearReceipts 2406.7 2567.7 2523.6 2104.4 2161.7 2302.5 2449.1 2774.0 3020.4 3188.2 3445.7 3618.5Outlays 2654.9 2729.2 2978.4 3520.1 3455.9 3599.3 3538.3 3454.2 3503.7 3702.3 3865.3 4063.8Surplus or Deficit (-) -248.2 -161.5 -454.8-1415.7-1294.2 -1296.8 -1089.2 -680.2 -483.4 -514.1 -419.6 -445.3 National Income & Products Accounts Basis, Calendar YearCurrent Receipts 2531.7 2660.8 2503.7 2227.8 2391.8 2519.5 2684.1 3113.0 3300.8 3450.1 3689.5 3860.6 Current Tax Receipts 1558.5 1637.1 1448.1 1163.8 1305.0 1501.3 1651.6 1811.8 2024.5 2165.6 2328.5 2410.3 Personal Current Taxes 1049.6 1164.4 1101.7 857.2 893.8 1076.6 1149.0 1286.8 1374.2 1475.2 1608.5 1712.9 Taxes - Corporate Income 395.0 362.8 233.6 200.4 298.7 299.4 369.5 384.9 497.3 529.2 548.7 513.5 Taxes - Production/Imports 99.2 94.6 94.0 91.4 96.8 108.6 115.0 120.9 134.1 141.6 150.8 162.5 Contributions for Soc. Ins. 905.7 947.3 974.4 950.8 970.9 904.0 938.1 1092.3 1149.4 1202.1 1277.9 1363.4 Income Receipts on Assets 28.9 33.4 33.9 48.5 54.6 56.4 53.6 164.8 78.1 45.9 42.3 40.9 Current Transfer Receipts 36.8 41.0 46.5 63.9 64.4 65.0 49.9 59.5 68.5 55.7 58.0 60.4 Surplus of Gov’t. Enterprises 1.8 2.0 0.8 0.7 -3.1 -7.1 -9.1 -15.3 -19.7 -19.3 -17.1 -14.3

Current Expenditures 2758.8 2926.4 3137.7 3476.6 3720.5 3763.7 3763.2 3762.1 3883.1 3997.7 4180.4 4394.4 Consumption Expenditures 763.9 798.3 879.8 933.7 1003.9 1006.1 1003.6 963.1 965.3 972.9 1002.6 1039.6 Defense 500.3 526.1 582.8 613.3 653.2 662.3 650.5 616.4 618.9 623.1 648.5 679.2 Nondefense 263.6 272.3 297.0 320.4 350.7 343.8 353.2 346.6 346.4 349.8 354.2 360.4 Transfer Payments 1571.4 1672.4 1820.3 2132.4 2281.7 2272.4 2278.3 2322.0 2419.6 2553.4 2679.6 2819.3 Government Social Benefits 1184.2 1258.9 1391.9 1608.9 1710.1 1727.3 1767.0 1806.8 1863.4 1947.8 2037.9 2148.4 To the Rest of the World 12.5 13.3 15.5 16.0 16.5 17.1 18.1 18.9 19.3 20.0 20.4 21.1 Grants-in-Aid To S&L Governments 340.8 359.0 371.0 458.1 505.3 472.5 444.4 450.0 500.9 534.5 568.5 595.8 To the Rest of the World 33.9 41.3 41.9 49.4 49.7 55.6 48.8 46.4 36.0 51.1 52.7 53.9 Interest Payments 372.4 408.2 388.0 353.6 380.6 425.7 423.8 417.4 441.4 414.5 440.4 476.8 Subsidies 51.1 47.5 49.6 56.9 54.3 59.5 57.6 59.7 56.9 56.9 57.7 58.7

Surplus or Deficit (-) -227.0 -265.6 -634.0-1248.8-1328.7 -1244.2 -1079.1 -649.1 -582.3 -547.7 -490.9 -533.8

Table 12. State and Local Government Receipts and Expenditures 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Billions of Current DollarsReceipts 1254.5 1321.3 1328.9 1268.1 1305.7 1368.3 1424.8 1471.8 1495.1 1539.2 1620.6 1707.3 As Share of GDP 9.1 9.1 9.0 8.8 8.7 8.8 8.8 8.8 8.6 8.5 8.5 8.5Personal Tax and Nontax Receipts 302.5 323.5 333.5 287.8 297.6 324.1 354.7 375.0 368.7 383.7 409.9 436.6Corporate Profits 59.2 57.9 47.4 45.6 47.7 50.2 53.2 55.3 57.3 64.8 67.3 65.3Indirect Business Tax and Nontax Accruals 892.7 940.0 947.9 934.8 960.4 994.0 1017.0 1041.6 1069.1 1090.7 1143.5 1205.4Contributions For Social Insurance 21.5 18.9 18.7 18.6 18.2 18.2 17.7 17.7 17.6 17.8 18.8 19.8Federal Grants-In-Aid 340.8 359.0 371.0 458.1 505.3 472.5 444.4 450.0 500.9 534.5 568.5 595.8

Expenditures 1850.3 1973.3 2074.1 2191.2 2235.9 2246.4 2293.8 2350.8 2430.7 2487.0 2578.0 2682.8 As Share of GDP 13.4 13.6 14.1 15.2 14.9 14.5 14.2 14.0 14.0 13.8 13.5 13.4Purchases 1640.2 1752.2 1847.6 1871.4 1870.2 1865.3 1877.8 1912.4 1956.1 1983.8 2060.4 2146.7Transfer Payments 403.9 433.3 455.4 492.6 523.8 530.4 540.6 565.4 615.1 656.6 689.5 724.0Interest Received 25.4 17.3 36.0 114.3 123.0 125.9 143.7 137.0 131.1 124.8 120.1 117.9Net Subsidies 11.5 25.6 25.0 22.8 21.4 17.9 16.6 14.8 15.0 14.9 14.0 13.2Dividends Received 2.1 2.2 2.6 2.2 2.3 2.7 3.4 3.7 4.0 4.1 4.1 4.2Net Wage Accruals

Surplus Or Deficit -39.4 -72.7 -165.1 -271.9 -237.3 -215.9 -232.6 -225.2 -224.8 -196.1 -157.4 -132.9



Table 13. U.S. Exports and Imports of Goods and Services 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Billions of Current Dollars Net Exports-Goods & Serv. -771.0 -718.6 -723.1 -395.5 -512.7 -580.0 -568.3 -508.2 -538.2 -505.1 -541.3 -595.1 Current Account Balance -806.7 -718.6 -686.6 -380.8 -443.9 -459.3 -460.8 -400.3 -410.6 -424.5 -422.9 -483.8 Merchandise Balance -850.1 -837.3 -850.6 -525.2 -670.2 -777.9 -778.9 -739.4 -770.8 -752.6 -800.1 -865.1

Exports-Goods & Services 1476.3 1664.6 1841.9 1587.7 1852.3 2106.4 2194.2 2262.2 2337.0 2287.4 2460.2 2638.3 Merchandise 1049.6 1166.4 1298.8 1065.1 1279.6 1466.9 1527.1 1562.8 1614.7 1523.9 1638.4 1752.8 Food, Feeds & Beverages 66.0 84.3 108.3 93.9 107.7 126.2 132.9 136.2 139.9 124.8 135.5 143.1 Industrial Supplies 279.1 316.3 386.9 293.5 388.6 485.3 482.4 492.1 501.3 446.4 504.4 563.3 Motor Vehicles & Parts 107.3 121.3 121.5 81.7 112.0 133.0 146.1 152.6 159.9 149.9 169.3 186.6 Capital Goods, Ex. MVP 339.5 360.0 383.7 316.7 375.9 413.8 433.2 429.6 437.8 428.7 448.5 476.1 Computer Equipment 47.6 45.5 43.9 37.7 43.8 48.5 49.3 48.1 48.9 45.9 47.9 55.5 Other 291.9 314.5 339.8 279.0 332.1 365.4 383.9 381.5 388.9 382.8 400.6 420.6 Consumer Goods, Ex. MVP 129.0 145.9 161.2 149.3 164.9 174.7 181.0 188.4 198.6 199.9 204.5 203.8 Other 64.2 65.7 63.3 55.2 58.6 53.4 57.2 59.1 64.7 56.1 57.6 59.7 Services 426.7 498.2 543.1 522.6 572.7 639.5 667.0 699.4 722.2 763.5 821.7 885.6

Imports-Goods & Services 2247.3 2383.2 2565.0 1983.2 2365.0 2686.4 2762.5 2770.4 2875.2 2792.5 3001.4 3233.4 Merchandise 1899.7 2003.8 2149.4 1590.3 1949.8 2244.7 2306.0 2302.3 2385.5 2276.5 2438.6 2617.9 Foods, Feeds & Beverage 76.1 83.0 90.4 82.9 92.5 108.3 111.1 116.0 126.8 125.0 121.2 127.0 Petroleum & Products 316.7 346.7 476.1 267.7 353.6 462.1 434.3 387.6 351.1 206.6 268.1 294.5 Indus Supplies Ex. Petr 293.5 297.9 318.7 196.6 249.4 292.7 288.9 291.2 314.2 303.6 322.6 340.2 Motor Vehicles & Parts 256.0 258.5 233.2 159.2 225.6 255.2 298.5 309.6 328.9 342.5 346.1 361.9 Capital Goods, Ex. MVP 394.1 414.6 423.2 343.4 419.1 477.9 511.6 510.9 542.5 556.4 600.4 656.3 Computer Equipment 101.6 105.5 101.2 94.2 117.3 119.7 122.3 121.2 121.5 123.1 129.0 135.0 Other 292.5 309.2 322.0 249.2 301.9 358.2 389.4 389.7 420.9 433.3 471.4 521.3 Consumer Goods, Ex. MVP 447.6 479.8 485.7 429.9 485.1 515.9 518.8 533.9 558.9 570.5 600.6 642.4 Other 87.2 88.8 86.5 80.0 93.1 97.1 102.6 106.1 109.8 120.0 129.5 144.0 Services 347.6 379.4 415.6 392.9 415.2 441.6 456.4 468.1 489.7 516.0 562.9 615.5

Billions of 2009 Dollars Net Exports-Goods & Serv. -794.3 -712.6 -557.8 -395.4 -458.8 -459.4 -452.5 -420.5 -452.6 -546.8 -628.2 -665.2 Exports-Goods & Services 1506.8 1646.4 1740.8 1587.7 1776.6 1898.3 1960.1 2019.8 2084.7 2118.2 2223.2 2328.3 Imports-Goods & Services 2301.0 2359.0 2298.6 1983.2 2235.4 2357.7 2412.6 2440.3 2537.3 2665.1 2851.5 2993.5

Exports and Imports -- % ChangeCurrent Dollars Exports 12.8 12.8 10.7 -13.8 16.7 13.7 4.2 3.1 3.3 -2.1 7.6 7.2 Imports 10.7 6.0 7.6 -22.7 19.3 13.6 2.8 0.3 3.8 -2.9 7.5 7.7Constant Dollars Exports 9.0 9.3 5.7 -8.8 11.9 6.9 3.3 3.0 3.2 1.6 5.0 4.7 Imports 6.3 2.5 -2.6 -13.7 12.7 5.5 2.3 1.1 4.0 5.0 7.0 5.0

Production Indicators - % ChangeU.S. Industrial Production 2.2 2.5 -3.4 -11.3 5.7 3.3 3.8 2.9 4.2 1.7 3.4 3.4Real GDP -- Industrial Countries 2.9 2.6 0.7 -3.4 2.9 2.2 1.1 1.3 1.9 1.9 2.1 2.1Real GDP -- Developing Countries 6.7 6.6 3.8 0.0 7.4 5.5 4.2 3.7 3.3 3.1 3.9 4.3

Price IndicatorsPrice Deflators (% Ch) Exports 3.4 3.2 4.6 -5.5 4.3 6.4 0.9 0.1 0.1 -3.7 2.5 2.4 Imports 4.1 3.4 10.5 -10.4 5.8 7.7 0.5 -0.8 -0.2 -7.5 0.4 2.6

Crude Oil Prices ($/barrel) 66.1 72.3 99.6 61.7 79.4 95.1 94.2 98.0 93.0 53.7 67.6 82.1Real U.S. Dollar Ex. Rate-Indust. Countries 1.05 0.98 0.93 1.00 0.99 0.92 0.95 1.00 1.04 1.23 1.23 1.19 %Change -2.4 -6.4 -5.3 7.8 -0.5 -7.9 3.8 4.6 4.3 18.6 -0.5 -2.9 Ex. Rate-Dev. Countries 1.12 1.04 0.94 1.00 0.95 0.87 0.87 0.86 0.87 0.95 0.94 0.92 %Change -5.1 -7.4 -9.5 6.3 -5.2 -8.2 -0.5 -1.2 2.2 8.2 -0.4 -2.1



Table 14. Price Indexes for GDP and Other Inflation Indicators (Percent Change) 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Implicit Price DeflatorsGDP 3.1 2.7 1.9 0.8 1.2 2.1 1.8 1.5 1.5 1.2 2.4 2.5

Consumption 2.7 2.5 3.1 -0.1 1.7 2.5 1.8 1.2 1.3 0.3 2.2 2.5 Durables -1.6 -2.0 -1.9 -1.7 -1.4 -0.9 -1.3 -1.8 -2.5 -2.3 -0.9 -0.7 Motor Vehicles 0.1 -0.6 -2.3 0.3 5.7 3.0 1.4 0.6 -0.4 0.1 1.2 1.3 Furniture -0.5 -0.8 -0.7 -0.4 -4.2 -1.6 -0.3 -2.0 -3.5 -2.1 -0.2 -0.2 Other Durables 1.5 2.6 3.3 1.1 0.4 3.2 0.5 -0.2 -1.6 -1.7 0.7 1.4

Nondurables 3.1 2.9 5.6 -2.6 3.1 5.9 2.4 0.2 0.6 -3.0 2.9 3.2 Food 1.7 3.9 6.1 1.2 0.3 4.0 2.3 1.0 1.9 1.3 2.7 2.5 Clothing & Shoes -0.4 -0.9 -0.8 0.9 -0.7 1.8 3.6 0.9 0.4 -0.2 0.8 0.5 Gasoline 12.9 8.3 18.0 -27.2 18.1 26.4 3.4 -2.6 -3.4 -26.7 9.5 9.9 Fuel 13.7 6.9 35.6 -31.5 17.0 27.2 1.3 -1.2 -0.4 -17.6 11.7 8.9 Motor Vehicle Fuel 12.8 8.4 16.6 -26.8 18.2 26.3 3.5 -2.7 -3.7 -27.4 9.3 10.0

Services 3.4 3.2 3.1 1.1 1.7 1.8 2.1 2.1 2.2 1.8 2.4 2.9 Housing 3.5 3.6 2.7 1.8 0.1 1.3 2.2 2.4 2.7 2.8 2.7 2.7 Utilities 8.0 3.1 7.8 -2.2 1.3 1.7 -0.2 3.2 4.3 -0.8 0.3 4.5 Electricity 12.1 3.9 6.4 3.0 0.2 1.7 -0.0 2.1 3.6 0.5 -0.5 4.0 Natural Gas 2.4 -1.2 13.8 -21.9 -2.0 -3.0 -9.7 4.7 7.5 -13.9 -3.7 7.5 Water & Sanit. 4.9 5.1 5.9 6.1 6.3 5.2 5.5 4.5 3.7 4.3 3.8 4.0 Health Care 3.0 3.7 2.7 2.7 2.5 1.8 1.8 1.4 1.2 0.6 1.7 2.4 Transportation 4.1 2.3 5.3 3.1 2.0 2.7 1.9 1.3 1.2 0.3 2.6 2.9 Recreation 3.4 2.8 3.1 1.2 1.1 1.7 2.7 1.7 1.9 1.5 2.8 3.0 Food & Accomm. 3.4 3.9 3.9 2.2 1.3 2.5 2.8 2.1 2.6 2.7 2.5 2.9 Financial & Insur. 2.7 2.9 1.1 -4.4 4.0 2.4 3.7 2.7 2.9 2.9 3.8 3.6 Other Services 4.0 3.1 4.6 2.8 3.0 2.5 2.5 2.8 2.4 2.3 2.9 3.2

Investment Deflators: Nonresidential 2.9 2.1 1.8 -0.3 -0.9 1.5 1.5 1.1 1.2 0.5 1.4 2.1 Structures 12.2 6.1 4.8 -2.2 -1.2 3.0 3.5 2.8 2.5 1.7 3.2 3.2 Equipment -0.3 0.3 0.1 1.3 -2.0 0.9 1.0 0.4 0.6 0.5 0.8 1.7 Intellectual Prop. 1.6 1.7 1.7 -0.8 0.5 1.3 1.0 0.8 1.2 -0.2 1.1 2.0 Residential 5.8 1.3 -1.5 -3.5 -0.4 0.8 0.9 5.0 5.9 3.0 2.6 3.3

Government Purchases 4.4 4.4 4.3 -0.3 2.7 3.0 1.5 1.2 1.2 0.3 2.3 2.8 Federal 3.3 3.0 3.0 -0.3 2.6 2.7 0.9 1.1 0.9 0.3 1.7 2.1 State & Local 5.0 5.2 5.1 -0.3 2.7 3.1 1.9 1.3 1.3 0.3 2.6 3.3

Exports 3.4 3.2 4.6 -5.5 4.3 6.4 0.9 0.1 0.1 -3.7 2.5 2.4Imports 4.1 3.4 10.5 -10.4 5.8 7.7 0.5 -0.8 -0.2 -7.5 0.4 2.6

Other Inflation Related IndicatorsConsumer Price Index All Urban 3.2 2.9 3.8 -0.3 1.6 3.1 2.1 1.5 1.6 0.0 2.6 3.1Producers Price Index 4.7 4.8 9.8 -8.7 6.8 8.8 0.5 0.6 1.0 -6.9 3.2 4.1

Nonfarm Sector IndicatorsWage Compensation 3.9 4.3 2.7 1.1 1.9 2.2 2.7 1.1 2.5 2.7 4.0 4.2Productivity 0.9 1.6 0.8 3.2 3.3 0.2 1.0 0.9 0.7 0.3 2.0 1.8Unit Labor Costs 3.0 2.7 2.0 -2.0 -1.3 2.1 1.7 0.3 1.8 2.4 1.9 2.4

Crude Oil Prices (dollars/barrel)West Texas Intermediate 66.10 72.28 99.61 61.69 79.41 95.07 94.21 97.96 92.97 53.69 67.61 82.12



Table 15. Producers Price Indexes 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Annual Percent ChangeAll Commodities 4.7 4.8 9.8 -8.7 6.8 8.8 0.5 0.6 1.0 -6.9 3.2 4.1Industrial Commodities 5.4 3.8 9.8 -9.0 7.0 8.0 0.0 0.4 0.6 -7.2 3.5 4.5Textiles & Apparel 1.4 1.0 2.4 0.5 1.7 7.6 0.3 0.8 1.5 -1.3 1.6 1.8Fuels 6.6 6.6 20.5 -25.8 17.1 16.0 -1.8 -0.2 -0.9 -22.9 8.0 10.0Chemicals 7.2 4.4 14.3 -6.5 7.5 11.5 0.5 0.9 0.7 -6.8 4.0 4.3Rubber & Plastics 6.9 0.8 7.0 -0.4 3.3 7.1 2.3 1.1 0.6 -1.5 1.3 2.8Lumber & Wood -1.1 -1.0 -0.6 -4.4 5.4 1.1 3.5 6.5 4.3 0.6 3.2 1.6Pulp & Paper 3.6 3.4 4.6 -0.5 5.0 3.5 -0.4 1.9 0.7 -0.5 2.2 3.9Metals & Products 12.9 6.5 10.1 -12.2 11.1 8.8 -2.7 -2.9 0.7 -4.7 0.4 2.2Equipment 2.0 0.9 1.9 1.2 -0.1 1.3 1.1 0.7 0.8 0.4 1.3 2.0Trans. Equipment 1.1 1.6 2.3 2.3 0.7 1.7 2.2 1.2 1.4 1.4 1.7 2.3

Farm -1.2 22.5 12.4 -16.5 12.2 23.6 3.2 1.4 1.1 -12.5 0.4 1.4Processed Foods & Feeds 0.4 7.3 9.3 -2.4 3.4 8.4 3.9 1.5 3.9 -2.0 2.3 1.1

By Stage of ProcessingCrude Materials 1.4 12.2 21.5 -30.5 21.2 17.5 -3.2 2.1 1.1 -20.7 6.2 6.6Intermediate Materials 6.4 4.0 10.3 -8.2 6.4 8.9 0.5 0.0 0.6 -6.8 2.1 3.4Finished Goods 2.9 3.9 6.4 -2.6 4.2 6.0 1.9 1.2 1.9 -4.1 2.8 3.6Consumers 3.4 4.5 7.4 -3.8 5.5 7.5 2.0 1.4 2.1 -5.7 3.2 4.0Producers 1.5 1.9 2.9 1.8 0.4 1.5 1.9 0.9 1.4 1.0 1.6 2.3

Table 16. Money, Interest Rates and Corporate Profits 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Billions of DollarsMoney Supply (M1) 1374.8 1372.6 1434.4 1637.7 1742.2 2010.0 2311.9 2545.2 2806.5 2991.5 2888.4 2736.9Money Supply (M2) 6847.8 7269.7 7766.0 8392.1 8601.0 9229.2 10019.0 10691.8 11349.6 12002.0 12370.1 12666.6

Percent ChangeMoney Supply (M1) 0.2 -0.2 4.5 14.2 6.4 15.4 15.0 10.1 10.3 6.6 -3.4 -5.2Money Supply (M2) 5.3 6.2 6.8 8.1 2.5 7.3 8.6 6.7 6.2 5.7 3.1 2.4

Interest Rates (Percent) Short-term Rates 3-Month Treas. Bills 4.73 4.35 1.37 0.15 0.14 0.05 0.09 0.06 0.03 0.21 1.20 2.87 Prime Bank Loans 7.96 8.05 5.09 3.25 3.25 3.25 3.25 3.25 3.25 3.33 4.21 5.95

U.S. Government Bond Yields 5 Year Maturity 4.75 4.43 2.80 2.19 1.93 1.52 0.76 1.17 1.64 1.62 2.47 3.57 10 Year Maturity 4.79 4.63 3.67 3.26 3.21 2.79 1.80 2.35 2.54 2.24 3.18 3.90 30 Year Maturity 4.87 4.84 4.28 4.07 4.25 3.91 2.92 3.45 3.34 2.93 3.77 4.27

State and Local Governments Bond Yields Domestic Municipal Bonds 4.41 4.39 4.85 4.62 4.29 4.51 3.73 4.26 4.25 3.94 4.83 5.38

Corporate Bond Yields Moodys AAA Corp. Bonds 5.59 5.56 5.63 5.31 4.94 4.64 3.67 4.24 4.16 3.83 4.77 5.50

Conventional Mortgage Rate 6.41 6.34 6.04 5.04 4.69 4.46 3.66 3.98 4.17 3.92 4.93 5.83

Corporate Profits (Billions of Dollars)Profits Before Taxes 1851.43 1748.43 1382.45 1472.58 1840.68 1806.80 2136.10 2235.33 2419.93 2722.81 2925.78 2861.28Inventory Valuation Adj. -35.68 -39.50 -36.95 6.68 -41.03 -68.30 -9.50 3.33 -0.45 7.31 -47.56 -38.09Profits After Taxes 1378.08 1302.88 1073.33 1203.13 1470.15 1427.70 1681.33 1761.08 1827.30 2091.06 2270.74 2242.19

JUNE 2015 REPORT

THE UCLA ANDERSON FORECAST FOR CALIFORNIA

When Will California Reach its Potential (Employment)?

Silicon Beach and the Los Angeles Economy

UCLA Anderson Forecast, June 2015 California–51

WHEN WILL CALIFORNIA REACH ITS POTENTIAL (EMPLOYMENT)?

When Will California Reach its Potential (Employment)?Jerry NickelsburgSenior Economist, UCLA Anderson ForecastAdjunct Professor of Economics, UCLA Anderson SchoolJune 2015

The current economic expansion has had an unusually large spike in the number of long-term unemployed.1 This roughly corresponds to the decline in manufacturing, the shrinkage of a construction sector bloated by the housing bubble, and the changes in the finance, legal and professional services sectors. There has been much policy discussion to the effect that more monetary or fiscal stimulus would be required to re-employ the long-term unemployed and these idle resources have created a large gap between potential and actual economic activity.

Another view that we at the Anderson Forecast have favored is that the coming of the information age has eliminated jobs that no amount of stimulus would bring back. Some examples of this are the GM/Toyota plant in Freemont being transformed into a highly automated, robot driven Tesla plant, the elimination of human toll takers on the Golden Gate Bridge, and the automated warehouse handlers in The Inland Empire.

So who are these long-term unemployed who are still looking for work or who are discouraged and have dropped out of the labor force? While there are not good data on it for the State of California, economics suggest they are the mid- to late-career Boomers. Gen-Xers and Millennials, viewing the changing employment landscape and the cost to move their skill sets into expanding sectors will have observed a long potential working life ahead of them and

ample time to earn a return on this investment. However, those who are late career do not have too many potential years left to recoup the cost obtaining new or enhanced skills and therefore their incentives are much lower. This puts them in a position where their current skills, rusting from years of disuse, have not been in high enough demand to bring them back to work. In this essay we re-examine the potential employment and economic growth with an eye to this shifting demographic and conclude that while the data are not crystal clear on where the level of the potential is, it is most likely considerably below that predicted by metrics previously employed.

We have been measuring this potential output/employ-ment gap in several ways. One has been by the difference between the trend employment and actual employment with the idea being that trend represents potential employment. By the first metric California, after a spectacular year of more than 3% growth in jobs, was at the end of 2014 ap-proximately 700K jobs from potential. As can be seen from the graph, this analysis is highly dependent on how the trend is identified and presumes that the potential grows that trend at the rate of the State’s population growth. Though illustra-tive of the gap, it is highly analyst dependent. Nevertheless, this analysis suggests that the State will reach potential at the end of the decade because the trend is growing at a rate only slightly slower than employment.

52–California UCLA Anderson Forecast, June 2015


Chart 1

Source: CA EDD and UCLA Anderson Forecast

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1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014

Milli

ons

CA ACTUAL VERSUS TREND EMPLOYMENT(CPS SURVEY, 000 PEOPLE EMPLOYED)

Total Employment Trend Total Employment

0.500

0.520

0.540

0.560

0.580

0.600

0.620

0.640

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

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Chart 2

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Chart 3

Source: U.S. Census, CA DoF and UCLA Anderson Forecast

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Another, perhaps better, measure is the employment to population ratio. This fell during the recession, as it does during all downturns, but has not come very far back towards its previous levels. If measured against its 1999 peak, po-tential employment in California is 1.7M jobs above actual employment. Measured against its level at the beginning of the 2008/2009 recession it is 900K shy, a larger gap than that measured by trend growth in employment.2 This metric does not rely on arbitrary intervals for calculating trends and is therefore free of being influenced by the very different growth patterns of California in the 70s, 80s, 90s and 00s.

An explanation for the downward trend in Chart 2 might be that Boomers are getting older, aging out of the work force and therefore demographics have shifted the equation. However, an examination of the data for Califor-nia suggests that this might not be the case. Chart 3 shows the demographics for California from the 2010 census and the 2015 Intercensal estimate. While it is true that boom-ers are aging, the Millennial Generation cohorts are just as large as the aging Boomer cohorts in California. Relative to the U.S., the California population is younger and the Boomers are being replaced at a rate of slightly more than 1 to 1 by Millenials.

On the surface these data seem convincing. The employment/population ratio is down by about 900K rela-tive to the pre-recession peak and the demographics do not suggest a downturn in the number of people available for employment. However, it is important to observe that we are counting the population as having the same characteristics today as it had a decade ago.

There are two numbers to consider in this type of analysis, the numerator – employment,3 and the denomina-tor, the population 15 and over as a proxy for population available to enter the work force. The only way the proxy works is if the ratio of people available to enter the work force to the population 15 and over remains constant. In this balance of this essay it is argued that it does not.

Before crunching some numbers consider an example. A 55 year-old factory worker skilled in assembly work and laid off in 2008 is 62 years old today. Suppose that aside from occasional temporary jobs he has been out of work for 7 years. Is that person really available for the labor mar-ket? While speculative, the answer is likely no. Were the assembly job he lost offered to him at something similar to the old wage, perhaps, but it won’t be. That assembly job is



no longer being done with manual/mechanical labor but by a robotic machine.4 So, though he did not want to retire at 55, he has been retired for sometime and life has adjusted. Moreover, he is now eligible to supplement his savings or pension with social security. An increase in aggregate demand and improving labor markets does not come with an attractive job for our factory assembly line worker left behind by the information revolution.

There are three factors affecting the actual and poten-tial employment to population ratio. First is the bulge in the population associated with the Boomers. What is important here is not that the Millennial cohort coming into prime working ages is about the same size as the Boomer cohort aging out, but that the Boomer cohort aging out is larger than the Silent Generation before them. Though more Boomers are working as a percentage of their cohort as compared to past cohorts of over 60-somethings, the issue is not those that are working who are already counted in potential, but those that are not. Boomers that are not working, and not realistically available for work, should not be counted as part of the support for potential employment and output. In other words, in the full employment equilibrium employment to

population ratio calculation those not realistically available ought not be counted.

Second, the returns to staying in school have expanded and there is now an incentive to not be available for work (and not be a support for potential employment and GDP) among the 15 to 18 year olds who are currently getting their education. The high school graduation rate in California has been edging up from a pre-recession 67 percent to 80 percent in 2014. It is unlikely that an improving labor market will diminish the differential returns to schooling as firms in this expansion are using more and more equipment and software to replace workers at the low end of the skill set. Moreover, it is a matter of public policy to discourage drop-outs and to encourage high school graduation. The same is true for post-secondary education. Thus, it is more realistic to measure population from age 19 rather than age 16 in calculating potential employment. To be sure, some 15 to 19 year olds are in the labor market and available, though their employment prospects are not good, but a larger percentage of 19 to 23 year olds are in school than before and are therefore not available. So while the exclusion of the high school aged population is not entirely correct, it may well be a reasonable approximation.

Chart 4

Source: CA Department of Education

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Chart 5

Source: DataQuick

The third change relates to women in the labor force. Though we do not have good data for California female labor force participation, on a national level, women have been dropping out of the labor force since the peak in 1998. This has accelerated during recessions but the downward trend of the last 17 years has continued to present. Clearly, women who want to work and are out of the labor force due to discouragement at job prospects are available as potential employment, but fewer are in the “want to work” category. The surveys of women out of the labor force5 are mixed. On the one hand, women respond to survey questions in a way that suggests that they increasingly value good jobs and a career, and on the other, the number of women who are saying family is the most important factor in their lives and they have chosen to remain at home doing non-market work such as raising children regardless of their market driven job prospects. This trend shows up in the national numbers and is embedded in the previously discussed California numbers.

What happens when we attempt to adjust the employ-ment to population ratios to account for these three factors? The dearth of data to forecast each of these trends puts us at a disadvantage. Will more teens stay in high school longer and more go to college regardless of the state of the labor market? Does the downward trend in female labor force participation

continue? And what about our 62 year-old factory worker? Do more Boomers opt for retirement amid improving 401(k) values driving their participation rates down, or does the wave of new seniors keep on working longer? What we do know is that these three forces are working to lower the equilibrium employment to population ratio and are part of the explanation for the failure of this metric, as commonly calculated, to show an economic recovery.

To get a sense of the consequences of these changes on potential employment levels, the first approximation is to compute the ratio based on the population ages 19 to 64. This eliminates youth who are staying in school longer and seniors who are working longer but is silent on the trend in female labor force participation. When this is done, the employment gap of 905K relative to 2007 estimated for 2014 shrinks to 557K. At the expected rate of growth of this demographic over the next five years6 and a 2% growth in employment, California hits the recovered point of actual and potential employment being the same, in September of next year. This estimate may be too pessimistic since much of the employment gap was made up during the first four months of 2015 as a consequence of California’s labor markets continued expansion at a rapid clip.



The analysis also points out the weakness of using trend unemployment. The sensitivity of the trend to the starting and ending date means that one can get just about any gap. Nevertheless, were the trend to be adjusted for the growth in the working age population it would bend over and the gap, however defined, would be smaller. Some cursory experimentation with this show the magnitude of the closing of the gap to be similar to that found using the employment to population ratio.

In spite of the uncertainty surrounding the magnitude of the forces affecting potential employment, the implication for the California forecast is clear. Absent immigration, it is not likely that after mid 2016 there will be a large pool of labor for employers to expand into. Because of this population growth constraint, the current forecast is being revised downward in late 2016 and 2017 towards the rate of population growth (in the identified demographic). The good news is the long wait for a recovery appears to be about over. However it does mean slower growth in the Golden State through the balance of this expansion.

Employment Retrospective

California’s employment picture continues to improve with 2015 net payroll job gains through April averaging 38,000 for an improvement of 151,500 jobs since the first

of the year. The unemployment rate has now fallen to 6.3%, a rate 0.9% above the U.S. rate. The gap has now shrunk from 1.4% in December and 1.6% 12 months ago.

Overall growth in employment, both measured as total number of people employed by the Household Survey and the total number of payroll jobs as measured by the Establishment Survey are still growing at faster rates than the U.S. Compared to other states with populations over five million, California has been continuously in the top 10 in the growth of payroll employment. April jobs numbers puts the State’s total employment at 4.3% above the previ-ous peak and the number of payroll jobs at 3.6% above the previous peak (Chart 7).

The creation of new jobs in the Golden State continues to be widespread as well. In the 12 months ending April 2015, the top sectors in job creation were health care and so-cial services, leisure and hospitality, administrative services, professional technical and scientific services, construction, retail and wholesale trade. Non-durable goods continued to be the underperforming sector as it has since the last reces-sion began. The three months ending April 2015 showed no significant deviation in sectoral growth from the annual data with the same five leading sectors outperforming all other sectors. The only exceptions were the erosion of some of the earlier net gains in education and finance.

Chart 6

Source: CA Department of Finance, UCLA Anderson Forecast, CA EDD

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Our estimate for the 2015 total employment growth is 2.5%, and for 2016 and 2017 the forecast is for 2.1% and 1.3%. Payrolls will grow more at about the same rate the three years. Real personal income growth is estimated to be 4.5% in 2015 and forecast to be 4.4% and 3.5% in 2016 and 2017, respectively.

The unemployment rate will hover around 6.2% through the balance of 2015. Unemployment will fall through 2016 and will average approximately 5.2% a slight decrease from our last forecast. In 2017 we expect the un-employment rate to be approximately 5.0%, approximately the same as the U.S.

Forecast

The current forecast is for continued steady gains in employment through the middle of 2016. The increase in U.S. growth rates from construction, automobiles, and busi-ness investment as well as higher consumer demand will continue to fuel our local economy. What this means is a steady decrease in the unemployment rate in California over the next eighteen months. We expect California’s unemploy-ment rate to be insignificantly different from the U.S. rate at 4.9% during the forecast period and employment growth to then be constrained by the growth in the U.S., immigration, and natural growth in the working age population.

Endnotes

1. http://bls.gov2. Typical employment to population ratio measures for U.S. data use the population age 16 and above. The annual breakdown of

population data for California is bracketed ages 9-14 and 15-18 and therefore the population numbers here begin at age 15 and not 16. http://www.dof.ca.gov

3. The employment numbers in this essay are from the Current Population (Household) Survey and measure the number of people employed including the self employed, farm labor and those employed in family owned businesses. The two other measures of employment, Non-Farm Payroll Jobs and the BEA Total Full and Part Time Employment yield the same results as reported here for the Household employment numbers.

4. Much has been written on this subject. See for example a review of Erik Brynjolfsson and Andrew McAfee’s work on this topic: http://www.technologyreview.com/featuredstory/515926/how-technology-is-destroying-jobs/

5. http://kff.org/other/poll-finding/kaiser-family-foundationnew-york-timescbs-news-non-employed-poll/ 6. http://www.pewresearch.org/fact-tank/2014/11/14/more-and-more-americans-are-outside-the-labor-force-entirely-who-are-they/ The 18-64 year old demographic is growing slower than the overall population of California.


SILICON BEACH AND THE LOS ANGELES ECONOMY

Silicon Beach and the Los Angeles EconomyWilliam YuEconomist, UCLA Anderson ForecastJune 2015

This report presents data to answer three questions: (1) How well has the high-tech industry grown across cities over the past several years? (2) What is the state of Silicon Beach? and (3) Will Silicon Beach propel the Los Angeles economy to long-term prosperity? In this report, we use the information sector to represent the high-tech industry for simplicity’s sake. Note that the information sector includes the publishing industry, motion pictures & sound record-ing, broadcasting, telecommunications, and data process-ing, hosting & related services. Some of the industries in the information sector are not high-tech, and some other sectors, like manufacturing and professional & business services, do include some high-tech jobs. So why use the information sector? We believe it is a good indicator of the high-tech industry because, like high-tech, information is a knowledge-based sector. That said, the information sector could be highly correlated to the high-tech industry.

For the L.A. economy particularly, the integration of the entertainment and high-tech industries make the in-formation sector a good proxy for our report. As of March 2015, the entertainment industry accounts for 61% of the information sector employment in L.A. County, telecom-

munication accounts for 12%, broadcasting 10%, publish-ing 7%, newspaper 4%, radio & TV broadcasting 8%, data processing, hosting & related services 3%, and cable 2%.

The Information Sector By County Across the Nation

Figure 1 shows the 2013 ranking of the information sector by employment1 across U.S. counties. L.A. County has the largest information sector employment (230,500), followed by New York County (Manhattan)’s 154,000, King County (Seattle)’s 95,000, and Santa Clara County (Silicon Valley)’s 77,400. Figure 2 displays the ranking of the infor-mation sector by each county’s total annual salary in 2013. The highest total compensation for information-sector work was in New York County at $19.8 billion, followed by L.A. County at $18.5 billion, Santa Clara at $16.4 billion, King County at $15.2 billion, San Mateo County (north of Silicon Valley) at $11.8 billion, and San Francisco County at $7.8 billion. Figure 3 presents the ranking of the information sector by annual salary per worker in 2013. The top two counties by worker salary are two Silicon Valley counties:



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Figure 1. The Ranking of the Information Sector by County Employment, 2013

Source: Census County Business Patterns

Figure 2 The Ranking of the Information Secotr by Total Salary of County, 2013

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San Mateo County, by far the leader with an annual salary of $317,000, and Santa Clara County, averaging $211,300 per worker. The next highest paying information sector counties are Seattle at $159,000, San Francisco County at $157,000, Alameda (East Bay) at $124,000, Middlesex County, Massa-chusetts (Boston) at $124,000, and Fairfax County, Virginia at $113,000. L.A. County ranks 21, with an average salary of $80,400, significantly lower than the leaders.

Figure 4 lists the ten counties with the highest informa-

tion sector employment growth from 2005 to 2013 from the 30 largest information-employing counties (shown in Figure 1). During this period, San Francisco’s employment grew by 101%, North Carolina’s Research Triangle by 58%, San Mateo by 50%, Santa Clara by 46%, Boston by 30%, Seattle by 23%, Salt Lake County by 21%, Denver by 21%, L.A. County by 17%, and Suffolk County, MA by 11%.


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Figure 3 The Ranking of the Information Sector by Salary Per Worker of County, 2013

Figure 5 displays the distribution of information-sector establishment sizes based on number of employees for selected counties. The notable fact for L.A. is that it has a much larger fraction (70%) of small businesses (1 to 4 employees) than New York (54%) and Alameda (52%), the next two highest. As a result, L.A. has a smaller fraction of medium sized businesses. The reason is unclear, but there are two possibilities: (1) L.A. has a disproportional share of the entertainment industry in the information sector, many of which are agents. That also explains why the average salary in L.A. information sector is lower than those in the Bay Area or Seattle as shown in Figure 3. (2) L. A. has vibrant entrepreneurs in the information sector.




Figure 4 The Top Ten Counties in Information Sector Growth by Employment, 2013

Figure 5 The Distribution of Establishment Size by Number of Employees, Selected Counties, 2013

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San Francisco County: 101%

Wake County, NC (Research Triangle): 58%

San Mateo County: 50%

Santa Clara County: 46%

Middlesex County, MA (Boston): 30%

King County, WA (Seattle): 23%

Salt Lake County, UT: 21%

Arapahoe County, CO (Denver): 21%

Los Angeles County: 17%

Suffolk County, MA (Boston): 11%

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Patents

In addition to the information sector, we can look at the number of utility patents granted in a county as a measure of technology and innovation in the region. Figure 6 shows the 30 counties with the highest number of utility patents2 in 2013. Santa Clara, not surprisingly, is first on the list with 12,855 patents, distantly followed by San Diego (4,805), King (3,886), L.A. (3,550), San Mateo (3,543), Middlesex (3,332), Alameda (2,918), and Orange (2,721). Controlling for the different sizes of counties, Figure 7 presents the ranking of 10 major counties of per capita patents granted.

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The percentage numbers in Figure 7 represent the

growth of per capita patents granted from 2005 to 2013. Note that Seattle (164%), San Diego (149%), San Mateo (122%), and Boston (103%) had robust innovation growth during this period, whereas L.A. grew at a slower rate.

Figure 6 30 Largest Counties Based on the Number of Utility Patents, 2013

Source: U.S. Patent and Trademark Office, http://www.uspto.gov/web/offices/ac/ido/oeip/taf/countyall/usa_county_gd.htm



Venture Capital Investment Venture capital investment data can also tell us about

trends and geography in the high-tech and innovation indus-tries. Figure 8 shows the venture capital investment in the nation from 1995Q1 to 2015Q1. We can see that since the Internet bubble and bust in 2000, venture capital investments have been on the rise. For those who worry about a repeat

of the tech bubble, note that the current investment nominal amount is only half the size of the bubble’s peak. Figure 9 breaks down 2014 investments by industry and shows that software got a disproportionate percentage of venture capital interest. That could correspond to the high-growth of information sector jobs in San Francisco over the past few years (Figure 4) because many of the startups in San Francisco are software companies.

Figure 7 Utility Patents Per Capita (Number Per 10,000 Residents), by County, 2000 to 2013

Source: Patent and Trademark Office

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Santa Clara County: 69, +78%San Mateo County: 47, +122%Middlesex County (Boston): 22, +103%King County (Seattle): 19, +164%Alameda County: 18.5, +75%San Diego County: 15, +149%Oakland County: 14, +45%Orange County: 8.7, +67%Los Angeles County: 3.5, +62%Cook County (Chicago): 3.1, +69%



Figure 8 Venture Capital Investment in the Early Stage, Annualized Amount, by Major Industry, from 1995 to 2015

Source: PwC/National Venture Capital Association Moneytree Report

Figure 9 Venture Capital Investment in the Early Stage by Industry, 2014


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0 5 10 15 20 25Telecommunications

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Business Products & Services

Networking & Equipment

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Semiconductors

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Computers and Peripherals

Consumer Product & Services

Industry & Energy

Medical Device & Equipment

IT Services

Media & Entertainment

Biotechnology

Software

($Billion)



Figure 10 displays the geographical distribution of venture capital investment last year. Silicon Valley and the Bay Area got $23.4 billion in investments, which accounts for almost half of all such capital invested in the U.S. in 2014. The next highest amounts of venture capital were $5 billion to the New York Metro Area, $5 billion to New Eng-land, and $2.8 billion to Los Angeles and Orange Counties. As a state, California received 56% of the nation’s venture capital investments.

Silicon Beach and Silicon Valley Figures 11 and 12 show the number of establishments

in the information sector by Los Angeles and Bay Area zip code in 2013. The darker the red color, the more information firms there are in that zip code. We can see a high-density (deep red color) of information firms in Burbank, West Hol-lywood, and West L.A (Silicon Beach). This is mostly owing to the fact that L.A. has such a high number of entertainment

Figure 10 Venture Capital Investment by Major Regions, 2014


firms as well as small establishments (discussed in Figure 5). In fact, in West L.A., Westwood, Santa Monica, and Venice (zip codes 90024, -025, -064, -066, -291, -401, -404, and -405), there are 1,462 information sector firms. Nevertheless, some of them are high-tech firms.

Figures 13 and 14 present the change in the number of information firms from 2007 to 2013 by zip code. The red color signifies growth in number, and the darker the color, the higher the growth. In contrast, the blue color represents a decline in information firms. We can see the darkest red colors in Glendale, downtown L.A., Silicon Beach, and Silicon Valley. If Silicon Beach can continue to grow in the coming years, L.A.’s economy will benefit.

Conclusions

The high-tech sector is growing in counties across the U.S., and L.A. is not among the top leaders in terms of patents, capital, or salary. However, there is a large in-formation sector in L.A., currently concentrated in vibrant small-sized firms. Silicon Beach is on the rise, and hope-fully it will continue to rise. As Silicon Valley expanded to benefit San Mateo and San Francisco, we need creative and vibrant Silicon Beach to expand to the rest of L.A. Since the information sector requires a highly-knowledgeable, highly-skilled, and highly-educated workforce, it pays better than the average industry job, which is vital for L.A. due to the high cost of living.

Since high-tech products and services are in demand worldwide, as a sector it will presumably continue to grow in the 21st century, and it can be a wealth creator for local economies. Resilient growth in this sector will set L.A. on course for long-term prosperity. If L.A. can expand its infor-mation sector to include a larger percentage of its population by growing more medium- and large-sized companies, we would see far-reaching benefits for the county, its residents, and the nation.

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Endnotes

1. The employment is based on paid employees in every March. 2. The utility patent is also called “patents for invention”, which are issued for the invention of a new and useful process, machine,

manufacture, or composition of matter, or a new and useful improvement thereof. In recent years, 90% of issued patents have been utility patents as opposed to other patents such as design patent, plant patents, etc.



Figure 11 The Number of Establishment of the Information Sector By Zip Code in Los Angeles, 2013

Source: Census Zip Code Business Patterns

Figure 12 The Number of Establishment of the Information Sector By Zip Code in Bay Area, 2013




Figure 13 The Change of Establishment Number in the Information Sector by Zip Code in Los Angeles from 2007 to 2013

Figure 14 The Change of Establishment Number in the Information Sector by Zip Code in Bay Area from 2007 to 2013




THE EVOLUTION OF CITY HUMAN CAPITAL INDEX ACROSS THE COUNTRY AND PUBLIC SCHOOL PERFORMANCE IN CALIFORNIA: EVIDENCE FROM 2005 TO 2013

The Evolution of City Human Capital Index Across the Country and Public School Performance in California:Evidence from 2005 to 2013 William YuEconomist, UCLA Anderson ForecastJune 2015

In this report, we summarize the First 5 LA/UCLA Anderson Forecast City Human Capital Index (CHCI) broken down by nation, state, metropolitan statistical area (MSA), county, principal municipal city, school district, and zip code in California from 2005 to 2013. The CHCI is computed based on the adult education attainment data from American Community Survey. Since its launch in 2012, we have been updating the data and have written several reports from various angles. The goal of CHCI is to provide a simple measurement of human capital across the country and over time. The interpretation of the CHCI is straightforward. On the whole, one tenth of the CHCI is roughly equal to one year of schooling for local adult residents, although the number is weighted to reflect the increasing return of higher education.

The methodology works as follows: For those whose formal education ended before 9th grade, we assign the number 5, for those ending between 9th and 12th grade, we assign 10; for those with a high school diploma, we assign 12; for those with some college, 13; for those with an associ-ate’s degree, 14; for those with a bachelor’s degree, 19; and for those with a graduate or professional degree, we assign 23. Detailed data and previous reports can be found on the Anderson Forecast website: (http://www.anderson.ucla.edu/centers/ucla-anderson-forecast).

CHCIs in the Nation and States

Figure 1 shows the CHCIs for the nation and the three biggest states in the country. In the U.S., CHCI has been steadily increasing, from 140 in 2005 to 143.3 in 2013. This points to two possibilities: (1) We are steadily building up

our human capital through educating our residents, and/or (2) The net immigration population is bringing in higher human capital. California’s CHCI is lower than the nation’s average. Its CHCI has increased from 137.9 in 2005 to 141.4 in 2013. Texas has an even lower CHCI level compared to the nation and California, but its CHCI has increased from 133.8 in 2005 to 138.4 in 2013, which is a stronger improve-ment than the nation and California. New York State has a higher CHCI level, with its CHCI increased from 143.6 in 2005 to 146.8 in 2013.

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2005 2006 2007 2008 2009 2010 2011 2012 2013United StatesCalifornia

TexasNew York

Figure 1 City Human Capital Index for the 3 Largest States in the U.S., 2005-2013

Source: Author’s calculation based on multiple years of American Com-munity Surveys.



Figure 2 displays the CHCI for the 50 states in 2013. Massachusetts has the highest human capital level (154) in the country followed by Colorado (152.4) and Maryland (152.1). West Virginia (133.6) has the lowest human capital level, preceded by Mississippi (134.6). Among 50 states, California ranks 37 for CHCI, which indicates a relatively weak state of human capital.

CHCIs in Metros

Figure 3 presents the CHCIs for the four largest metros: New York, L.A., Chicago, and Dallas from 2005 to 2013. During this period, L.A. has the largest gain of CHCI (+4.8). Nevertheless, its CHCI in 2013 is still lower than those of the other three metros. Figure 4 shows the ranking of CHCIs for the 30 largest metros in 2013. The leading

130 135 140 145 150 155West Virginia

MississippiArkansasLouisianaKentucky

NevadaAlabama

TexasOklahoma

TennesseeIndiana

New MexicoSouth Carolina

CaliforniaArizona

IdahoFlorida

North CarolinaOhio

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South DakotaIowa

United StatesNorth Dakota

MichiganPennsylvania

WisconsinMaine

NebraskaAlaska

WyomingDelaware

Rhode IslandMontana

IllinoisKansasOregonHawaii

New YorkUtah

WashingtonMinnesota

New JerseyNew Hampshire

VirginiaVermont

ConnecticutMarylandColorado

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Figure 2 2013 City Human Capital Index for 50 States in the U.S.

Source: Author’s calculation based on the 1-year American Community Survey, 2013.

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Figure 3 City Human Capital Index for the 4 Largest Metros in the U.S., 2005-2013

Source: Author’s calculation based on multiple years of American Com-munity Surveys.



metro in CHCI is Washington DC (162), followed by Boston (158.4), San Francisco (156.5), and Seattle (154). New York and Chicago are in the middle of the ranking. L.A.’s CHCI (139.7) comes at the bottom, only trailed by San Antonio (139.4) and Riverside (132.4). Appendix A lists the CHCI ranking for the 100 largest metros in the U.S, with a wide range of average human capital from Washington DC’s 162 to McAllen, Texas’s 116.

CHCIs in Counties

Figure 5 exhibits the CHCIs for the 30 largest coun-ties. The county with the highest CHCI is New York County (Manhattan: 168.4) followed by Middlesex County (Boston: 165.9) and King County (Seattle: 161.2). L.A. County’s CHCI is 137.2, which lags far behind those leading coun-ties. Appendix A lists the CHCI ranking for the 100 largest counties in the U.S, with a wide range of average human

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capital from Fairfax County, Virginia’s 172 to Hidalgo County, Texas’s 116.

CHCIs in Cities

Figure 5 lists the CHCIs for the 48 major municipal cities in California in 2012. The difference of human capital level among municipalities is larger than those in metros and counties across the nation. Palo Alto has the highest CHCI: 197.1 followed by Cupertino’s 191, and Berkeley’s 183. Three cities in the L.A. metro are in the leading group of human capital: Santa Monica (178), Newport Beach (177.7), and Irvine (177). Several big cities are in the middle: San Francisco (161.1), San Diego (153.7), Oakland (147.9), San Jose (147.2), Long Beach (138.3), and L.A. City (136.6). Three cities in Southern California are at the bottom: Ontario (119.7), San Bernardino (119.6), and Santa Ana (108).



Figure 5 2013 City Human Capital Index for the 30 Largest Counties in the U.S.


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100 110 120 130 140 150 160 170 180 190 200Santa Ana City, Los Angeles Metro

San Bernardino City, Riverside MetroOntario City, Riverside Metro

Victorville City, Riverside MetroAnaheim City, Los Angeles Metro

Chino City, Riverside MetroHayward City, San Francisco Metro

Riverside City, Riverside MetroGardena City, Los Angeles Metro

Carson City, Los Angeles MetroMonterey Park City, Los Angeles Metro

Los Angeles City, Los Angeles MetroSan Leandro City, San Francisco Metro

Long Beach City, Los Angeles MetroCorona City, Riverside Metro

South San Francisco City, San Francisco MetroOrange City, Los Angeles Metro

Glendale City, Los Angeles MetroTemecula City, Riverside Metro

Costa Mesa City, Los Angeles MetroSan Jose City, San Jose Metro

Oakland City, San Francisco MetroTustin City, Los Angeles Metro

Burbank City, Los Angeles MetroRedwood City City, San Francisco MetroFountain Valley City, Los Angeles Metro

Milpitas City, San Jose MetroSan Diego City, San Diego Metro

Redlands City, Riverside MetroSan Rafael City, San Francisco Metro

Torrance City, Los Angeles MetroSan Francisco City, San Francisco Metro

Pasadena City, Los Angeles MetroArcadia City, Los Angeles Metro

Santa Clara City, San Jose MetroCarlsbad City, San Diego Metro

Pleasanton City, San Francisco MetroSunnyvale City, San Jose Metro

Walnut Creek City, San Francisco MetroMountain View City, San Jose Metro

Irvine City, Los Angeles MetroNewport Beach City, Los Angeles Metro

Santa Monica City, Los Angeles MetroSan Ramon City, San Francisco Metro

Berkeley City, San Francisco MetroCupertino City, San Jose Metro

Palo Alto City, San Jose Metro

Figure 6 2012 City Human Capital Index for Major Cities in California

Source: Author’s calculation based on the 3-year American Community Survey, 2011-13.



CHCIs in Zip Codes

Figure 7 depicts the CHCIs by zip codes in 2011 (from 5-year ACS data 2009 to 2013). By and large, red colored areas represent the zip codes with above-average human capital while blue colored areas represent the zip codes with below-average human capital. The darker the

red color, the higher its CHCI. The darker the blue color, the lower its CHCI. The darkest red color represents zip codes with CHCI higher than 168. The darkest blue color represents zip codes with CHCI lower than 114. It is clear to see a bifurcated California, a Coastal California with high human capital versus an Inland California with low human capital, especially in the Central Valley.

Figure 7 2011 City Human Capital Index by Zip Codes in California.

Source: Author’s calculation based on the 1-year American Community Survey, 2013



Figure 8 takes a closer look at CHCIs in the L.A. metro area, and Figure 9 does the same for the Bay area. In L.A., we can see there are several big and deep blue areas with staggeringly low human capital: (1) South and East L.A., (2) San Fernando Valley (East of 405 Highway), (3) San Gabriel Valley, and (4) Anaheim-Santa Ana area. In contrast, West L.A., Pasadena, and Irvine areas have high

human capital. On the other hand, Figure 9 shows that most of the zip codes in the Bay area have above-average human capital. The distressed zip codes with the deepest blue color are only seen in San Jose and Oakland, much fewer and smaller than those in L.A. Therefore, on average, L.A. has a lower human capital level than the Bay area, which echoes the facts shown in Figure 4.

Figure 8 2011 City Human Capital Index by Zip Codes in Los Angeles Area




Figure 9. 2011 City Human Capital Index by Zip Codes in Bay Area




Rank Zip Code CHCI '11 Audl t Population '11 CHCI Change '09-'11 Population Change '09-'111 90402 192.2 9051 -0.8 -5312 90272 191.2 16115 2.1 -2553 90077 190.2 6052 1.4 -764 90049 188.7 27035 2.9 -8945 91020 188.0 13308 3.3 -1996 90274 186.8 18132 1.0 -1947 90266 186.1 25131 0.9 8788 91105 186.1 9435 1.3 8199 90403 185.3 19869 2.6 -73

10 91108 185.0 9530 -2.8 11911 90263 184.4 144 -40.7 -412 90024 183.1 23074 0.5 8613 90212 181.7 9028 -0.2 42714 90254 181.5 15095 0.0 3015 90292 179.9 17920 -1.7 -47816 90275 179.3 30625 -0.4 78417 91302 177.9 16903 -2.1 718 90265 177.6 12417 0.2 3419 90405 177.2 21909 -1.0 34720 91436 176.4 10496 -1.5 296

263 90040 104.2 7778 1.5 163264 91331 103.2 59394 2.2 3244265 90221 102.2 27994 1.3 738266 90262 102.2 38125 1.8 224267 90280 101.8 55816 0.5 2003268 91733 101.5 27194 0.9 657269 90304 100.7 15715 1.3 655270 90003 100.4 34385 1.7 595271 90002 100.2 25501 0.3 2087272 90033 99.8 27067 0.6 106273 90022 98.4 40761 0.8 1956274 90037 98.0 35294 0.4 1277275 90255 97.5 43344 -0.1 735276 90063 96.5 31292 -1.3 662277 90023 96.3 25700 1.8 755278 90201 95.8 55981 1.7 1276279 90270 93.5 14597 3.0 -70280 90001 92.7 29401 0.3 191281 90011 89.2 54474 -0.6 164282 90058 84.7 1591 -5.3 -165

Table 1 CHCI and Population Levels and Changes from 2009 to 2011 in Zip Codes by Ranking of 2011 CHCI in Los Angeles County

Source: Author’s calculation based on multiple years of American Community Surveys.



Rank Zip Code CHCI '11 Audl t Population '11 CHCI Change '09-'11 Population Change '09-'111 93553 126.8 510 18.9 1962 90014 140.6 5615 14.3 7803 91330 169.3 247 12.5 1684 90094 167.7 100 11.2 -1155 90742 163.5 776 9.2 -1496 91011 164.8 980 7.7 257 90015 119.1 11841 7.3 7078 91354 164.9 18533 5.5 5919 90041 153.2 19406 5.5 31

10 90007 118.1 19534 5.1 43811 90012 125.5 21973 4.9 30212 91602 166.7 14226 4.7 3613 90241 131.3 28021 4.6 61514 91340 109.1 20866 4.4 156015 90013 141.0 9170 4.3 202316 93563 131.4 283 4.3 1117 91103 136.3 17969 4.2 -52118 90211 165.8 5664 4.1 11619 91106 167.6 17413 3.9 -20820 90010 158.4 3499 3.9 738

263 90745 130.6 37746 -2.1 502264 91204 131.7 11436 -2.2 232265 93552 140.0 1053 -2.4 10266 90732 149.4 14942 -2.6 -381267 93535 132.2 2447 -2.7 284268 91776 129.4 27387 -2.8 286269 91108 185.0 9530 -2.8 119270 90020 141.6 28254 -3.0 -594271 91042 137.2 20879 -3.1 1061272 91208 165.3 11275 -3.4 303273 90802 147.8 7298 -3.5 -201274 91205 134.6 26842 -3.6 -403275 91207 163.5 8063 -4.2 277276 90067 167.1 2070 -4.7 -115277 90058 84.7 1591 -5.3 -165278 93534 121.4 764 -7.6 38279 91030 152.2 5953 -8.3 -10280 91001 133.8 154 -10.8 125281 91210 164.2 328 -11.0 182282 90263 184.4 144 -40.7 -4

Table 2 CHCI and Population Levels and Changes from 2009 to 2011 in Zip Codes by Ranking of CHCI Change ’09-’11 in Los Angeles County




Rank Zip Code CHCI '11 Audl t Population '11 CHCI Change '09-'11 Population Change '09-'111 93536 136.0 50386 -1.8 35352 91331 103.2 59394 2.2 32443 91744 111.0 50473 0.3 26304 90006 107.9 39022 1.6 22465 91350 150.1 22670 -0.5 22416 90044 110.4 50501 1.4 21597 90002 100.2 25501 0.3 20878 90013 141.0 9170 4.3 20239 90806 118.3 53880 0.0 2019

10 90280 101.8 55816 0.5 200311 90814 107.4 32744 0.5 197712 90022 98.4 40761 0.8 195613 90066 158.5 43200 0.0 186514 90057 109.3 30323 -1.6 184815 91343 127.1 38536 -1.2 181716 91601 144.5 26520 1.0 179017 91342 121.1 54574 0.7 176418 91335 128.8 49747 -0.5 172219 91505 153.2 22714 1.0 161220 91405 123.1 33895 -1.1 1591

263 90068 173.7 17905 -1.7 -316264 91803 138.5 21565 3.9 -327265 91702 123.0 33041 -1.6 -353266 90732 149.4 14942 -2.6 -381267 90248 132.2 6998 1.4 -385268 91205 134.6 26842 -3.6 -403269 91501 148.9 14869 0.5 -409270 91606 125.9 29950 0.6 -440271 90065 131.8 30750 0.3 -477272 90292 179.9 17920 -1.7 -478273 91103 136.3 17969 4.2 -521274 90402 192.2 9051 -0.8 -531275 90034 161.7 42829 1.2 -534276 90038 130.0 19874 1.3 -567277 90020 141.6 28254 -3.0 -594278 90039 153.4 20655 3.3 -718279 90049 188.7 27035 2.9 -894280 90005 125.5 27262 -0.1 -1095281 91746 110.5 18038 -2.0 -1228282 90042 129.1 40711 0.8 -1652


Table 3 CHCI and Population Levels and Changes from 2009 to 2011 in Zip Codes by Ranking of Population Change ’09-’11 in Los Angeles County



Appendix C lists the CHCIs and populations of all the zip codes in L.A. County from 2009 to 2011 along with their changes in the same period. Table 1 shows the top 20 zip codes and bottom 20 zip codes based on the CHCI in 2011. As shown in Figure 8, highly educated zip codes are mostly in West L.A., in which 90402 (Santa Monica) and 90272 (Pacific Palisades) have the highest CHCIs:192 and

191, respectively. On the other hand, 90011 (South L.A.) and 90058 (Vernon) have the lowest CHCIs: 89 and 85, respectively. Note that all those 20 bottom zip codes in L.A. County have CHCIs below 104. That means the bottom frac-tion of L.A. has a human capital score much lower than the nation’s lowest human capital metro: McAllen, Texas at 116.

Figure 10 2009 City Human Capital Index by Zip Codes in Downtown Los Angeles Area


Figure 11 2011 City Human Capital Index by Zip Codes in Downtown Los Angeles Area




Table 2 displays the top 20 zip codes and bottom 20 zip codes based on the CHCI change from 2009 to 2011. It is worth noting that we can see a revival of downtown L.A. For instance, 90014 (downtown L.A.) has seen its CHCI increase by 14.3, 90015 (Staple Center)’s CHCI increase by 7.3, 90007 (USC neighborhood)’s CHCI increase by 5.1, and 90012 (Chinatown/Little Tokyo)’s CHCI increased by 4.9. As we know, the trend all over the country in the recent decade is the rebirth of urban cores, from New York and Chicago, to Los Angeles, where we see young Americans choosing to stay centralized for improved urban amenities and the convenience of nearby work. Figure 10 shows the CHCIs in Downtown L.A. in 2009 while Figure 11 shows the CHCIs in 2011. We can see some color change, which indicates the improvement of CHCI.

CHCIs in School Districts and Academic Performance Index

Appendix D lists the CHCI ranking in 115 major unified school districts (USD) in California. The number 1 USD is Palo Alto USD with a CHCI of 199.6, followed by Berkeley USD (181), Santa Monica-Malibu USD (178.3), Irvine USD (176), Redondo Beach USD (170.2), Pasadena USD (161.7), and Torrance USD (159.1). Los Angeles USD, the largest school district in California, ranked 75th with a CHCI of 133.5 which is even lower than Los Angeles City’s CHCI of 136.6. That is because LAUSD also covers some lower educated neighborhoods outside the city boundary. Compton USD (CHCI: 111.5), Baldwin Park USD (111.2), Santa Ana USD (110), and Lynwood USD (103) are at the bottom with the lowest CHCIs.

As we mentioned in previous reports, CHCI can pro-vide a simple indicator of aggregate characteristics of local residents. Unfortunately, it is also a predictor to foretell the academic performance of local children. Figure 12 presents the correlation between a public school’s parents’ CHCI in 2012 and the Base Academic Performance Index (API) in 2012 over about 10,900 public schools in California. Each blue circle represents a school and their location of CHCI and API. The red line represents the predicted (regression) line of children’s API outcome based on the CHCI of the

parents of the children in that school. The positive regres-sion line implies that when adult parents’ CHCI increases from 100 to 200, for instance, the children’s API would be expected to increase from 695 to 940.

That said, if a child is born to a less educated parents living in a neighborhood with poor human capital, by and large, he or she is less likely to perform well in the public school. The upward-sloping line means that income mobility across generations is less likely to happen. More importantly, we also can see a vast variation across the predicted line given the same level of CHCI. For example, given the CHCI of 145, our model predicts, children’s API would be around 800 in that school (on the red line). However, we can see some schools’ API reach 900, which is performing quite well, whereas others’ are only 700, which is under-performing.

For those schools who are out-performing the predic-tion, we need to find out the reasons and encourage others schools to follow suit. For those who are under-performing, we need to make an effort to stop this slippery slope. Otherwise, it will perpetuate a cycle of lower educational achievement for the next generation. We need to avoid this cycle. Note that the red line only represents the average level of California’s public education. It is far from a golden standard to aim for. In fact, it is a low bar because compared to other states, California is under-performing given the consideration of its residents’ CHCIs.

In short, we need to commit ourselves to improving the quality of public schools all over California, and especially in L.A. because of its low CHCI. By doing so, we can en-hance the human capital in the cities where at-risk children are growing up and offer them a better future. Figures 13-18 list the correlations between CHCI and API from 2006 to 2011 in California. Assuming the standard of API remains the same, we do see an improving of input (CHCI) to out-put (API). That is, the red line is rising. Figure 19 shows the evolution of CHCI and API on average in California. Despite the setback of CHCI in 2010 and 2011, we can see the continual improvement of API. From 2006 to 2012, the average API in California increases from 721 to 791.



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Figure 12 The Correlation between Public-School Parents’ CHCI and Students’ Base API among Public Schools in 2012 in California






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I 2010

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139 139.5 140 140.5 141 141.5 142 142.5 143 143.5 144 144.5

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Public School Parents CHCI

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Figure 19 Public School Parents’ CHCI and Academic Perfor-mance Index in California from 2006 to 2012




Conclusions

The report presents a compressive measurement of the human capital index by nation, metro, county, city, zip code, and school district across California and the U.S. from 2005 to 2013. Furthermore, the take-away points of this report are as follows:

• California’s human capital level is lower than the na-tion’s average.

• Los Angeles’s human capital level is among the lowest in the major metros in the U.S.

• There is a significant disparity of human capital level in California and Los Angeles.

• Over time, human capital is improving steadily and slowly across the nation.

• In California, parents’ and communities’ human capital is highly correlated with children’s academic perfor-mance in public schools. However, dispersion among schools can teach us a lesson. We need to learn from those schools who are outperforming and improve those who are underperforming.

Endnotes

1. The education attainment data is from Census’ American Community Survey (ACS). We use ACS 2005 to calculate CHCI for 2005; we use ACS 3-year 2005-2007 (where mid-year is 2006) to calculate CHCI for 2006; we use ACS 3-year 2005-2007 (where mid-year is 2006) to calculate CHCI for 2006; we use ACS 5-year 2005-2009 (where mid-year is 2007) to calculate CHCI for 2007; we use ACS 5-year 2006-2010 (where mid-year is 2008) to calculate CHCI for 2008; we use ACS 5-year 2007-2011 (where mid-year is 2009) to calculate CHCI for 2009; we use ACS 5-year 2008-2012 (where mid-year is 2010) to calculate CHCI for 2010; we use ACS 5-year 2009-2013 (where mid-year is 2011) to calculate CHCI for 2011; we use ACS 3-year 2011-2013 (where mid-year is 2012) to calculate CHCI for 2012; we use ACS 1-year 2013 to calculate CHCI for 2013.

2. Based on the self-report of students for their parents’ education level.



Ranking Metro Name (100 Largest Metros) 2013 CHCI Adult Population1 Washington-Arlington-Alexandria, DC-VA-MD-WV Metro Area 162.0 40140082 Boston-Cambridge-Quincy, MA-NH Metro Area 158.4 32320663 Madison, WI Metro Area 158.1 4147684 San Jose-Sunnyvale-Santa Clara, CA Metro Area 157.8 12978395 Bridgeport-Stamford-Norwalk, CT Metro Area 157.6 6351556 San Francisco-Oakland-Fremont, CA Metro Area 156.5 32027397 Raleigh-Cary, NC Metro Area 156.3 7927598 Seattle-Tacoma-Bellevue, WA Metro Area 154.0 24797309 Minneapolis-St. Paul-Bloomington, MN-WI Metro Area 154.0 231245910 Denver-Aurora-Broomfield, CO Metro Area 153.6 182027511 Austin-Round Rock-San Marcos, TX Metro Area 153.0 122216012 Colorado Springs, CO Metro Area 152.8 43247613 Baltimore-Towson, MD Metro Area 152.2 188803014 Portland-South Portland-Biddeford, ME Metro Area 151.5 37212015 Hartford-West Hartford-East Hartford, CT Metro Area 151.5 83297416 Albany-Schenectady-Troy, NY Metro Area 151.3 59831717 Des Moines-West Des Moines, IA Metro Area 150.3 39482118 Portland-Vancouver-Hillsboro, OR-WA Metro Area 150.2 159030419 Kansas City, MO-KS Metro Area 149.1 136607320 Philadelphia-Camden-Wilmington, PA-NJ-DE-MD Metro Area 149.1 408945821 Rochester, NY Metro Area 149.1 73242422 St. Louis, MO-IL Metro Area 148.7 190204223 New Haven-Milford, CT Metro Area 148.6 59140024 Pittsburgh, PA Metro Area 148.5 169055825 New York-Northern New Jersey-Long Island, NY-NJ-PA Metro Area 148.4 1365110826 Columbus, OH Metro Area 148.4 129988527 Atlanta-Sandy Springs-Marietta, GA Metro Area 148.3 358386228 Charleston-North Charleston-Summerville, SC Metro Area 148.2 47734829 Omaha-Council Bluffs, NE-IA Metro Area 148.2 58131630 Urban Honolulu, HI Metro Area 148.0 66617231 Worcester, MA Metro Area 147.9 62932232 Milwaukee-Waukesha-West Allis, WI Metro Area 147.8 104863133 Chicago-Joliet-Naperville, IL-IN-WI Metro Area 147.7 633469434 Buffalo-Niagara Falls, NY Metro Area 147.3 78304535 San Diego-Carlsbad-San Marcos, CA Metro Area 147.0 211844036 Salt Lake City, UT Metro Area 147.0 69832037 Syracuse, NY Metro Area 147.0 44165838 Richmond, VA Metro Area 146.6 84632639 Ogden-Clearfield, UT Metro Area 146.3 36505240 Virginia Beach-Norfolk-Newport News, VA-NC Metro Area 146.3 111760441 Cincinnati-Middletown, OH-KY-IN Metro Area 146.3 141181142 North Port-Bradenton-Sarasota, FL Metro Area 146.1 55314643 Nashville-Davidson--Murfreesboro--Franklin, TN Metro Area 146.1 116570944 Columbia, SC Metro Area 146.0 51665745 Boise City-Nampa, ID Metro Area 145.8 41459846 Indianapolis-Carmel, IN Metro Area 145.8 128334547 Cleveland-Elyria-Mentor, OH Metro Area 145.6 142644348 Grand Rapids-Wyoming, MI Metro Area 145.6 65317549 Sacramento--Arden-Arcade--Roseville, CA Metro Area 145.4 145723850 Albuquerque, NM Metro Area 145.3 601086

Appendix A 2013 City Human Capital Index for the 100 Largest Metros in the U.S.




Ranking Metro Name (100 Largest Metros) 2013 CHCI Adult Population51 Tucson, AZ Metro Area 145.2 65565652 Harrisburg-Carlisle, PA Metro Area 145.2 38542653 Akron, OH Metro Area 145.1 47629854 Charlotte-Gastonia-Rock Hill, NC-SC Metro Area 145.1 154344055 Detroit-Warren-Livonia, MI Metro Area 144.9 291206056 Jacksonville, FL Metro Area 144.4 94642357 Jackson, MS Metro Area 144.3 37201158 Palm Bay-Melbourne-Titusville, FL Metro Area 144.2 40310959 Dallas-Fort Worth-Arlington, TX Metro Area 144.0 433240460 Spokane, WA Metro Area 143.9 35974161 Dayton, OH Metro Area 143.9 54015062 Wichita, KS Metro Area 143.9 40994263 Birmingham-Hoover, AL Metro Area 143.9 76904064 Springfield, MA Metro Area 143.7 40742865 Orlando-Kissimmee-Sanford, FL Metro Area 143.6 151523066 Little Rock-North Little Rock-Conway, AR Metro Area 143.5 47730467 Phoenix-Mesa-Glendale, AZ Metro Area 143.2 286260068 Tampa-St. Petersburg-Clearwater, FL Metro Area 142.8 203551969 Allentown-Bethlehem-Easton, PA-NJ Metro Area 142.7 57078770 Oxnard-Thousand Oaks-Ventura, CA Metro Area 142.6 54861771 Louisville/Jefferson County, KY-IN Metro Area 142.5 86116572 Oklahoma City, OK Metro Area 142.3 85189373 Providence-New Bedford-Fall River, RI-MA Metro Area 142.2 109962074 Knoxville, TN Metro Area 141.9 58463175 Miami-Fort Lauderdale-Pompano Beach, FL Metro Area 141.6 409076076 Toledo, OH Metro Area 141.6 39626377 Tulsa, OK Metro Area 141.4 63191878 Baton Rouge, LA Metro Area 141.2 52423079 Greensboro-High Point, NC Metro Area 141.2 49613580 Memphis, TN-MS-AR Metro Area 141.0 86372481 Houston-Sugar Land-Baytown, TX Metro Area 140.8 398484882 New Orleans-Metairie-Kenner, LA Metro Area 140.8 84631783 Greenville-Mauldin-Easley, SC Metro Area 140.8 56565484 Cape Coral-Fort Myers, FL Metro Area 140.1 48682785 Scranton--Wilkes-Barre, PA Metro Area 139.8 39601286 Los Angeles-Long Beach-Anaheim, CA Metro Area 139.7 869522987 Winston-Salem, NC Metro Area 139.6 44030988 Augusta-Richmond County, GA-SC Metro Area 139.4 38231789 San Antonio-New Braunfels, TX Metro Area 139.4 144419790 Deltona-Daytona Beach-Ormond Beach, FL Metro Area 138.8 43870191 Chattanooga, TN-GA Metro Area 138.6 37284692 Youngstown-Warren-Boardman, OH-PA Metro Area 136.6 39157693 Las Vegas-Paradise, NV Metro Area 136.0 135312994 Lakeland-Winter Haven, FL Metro Area 133.0 42492095 Riverside-San Bernardino-Ontario, CA Metro Area 132.4 270397696 El Paso, TX Metro Area 127.9 49287297 Stockton, CA Metro Area 127.8 43166598 Fresno, CA Metro Area 127.8 56899399 Bakersfield-Delano, CA Metro Area 123.5 510663

100 McAllen-Edinburg-Mission, TX Metro Area 116.2 448031

Appendix A 2013 City Human Capital Index for the 100 Largest Metros in the U.S.




County Name (100 Largest Counties) 2013 CHCI 2013 PopulationAlameda County, California 154.2 1086581Allegheny County, Pennsylvania 154.7 878367Baltimore County, Maryland 151.2 566661Bergen County, New Jersey 158.8 649902Bernalillo County, New Mexico 147.3 450240Bexar County, Texas 139.1 1140845Bronx County, New York 125.9 887739Broward County, Florida 144.9 1279640Bucks County, Pennsylvania 153.0 441722Clark County, Nevada 136.0 1353129Cobb County, Georgia 155.2 471805Collin County, Texas 162.0 551215Contra Costa County, California 151.7 739577Cook County, Illinois 146.9 3538859Cuyahoga County, Ohio 147.1 873009Dallas County, Texas 137.8 1564783Davidson County, Tennessee 150.4 446383DeKalb County, Georgia 153.5 476154Denton County, Texas 154.1 464097Denver County, Colorado 154.9 455345District of Columbia 169.7 453014DuPage County, Illinois 160.2 628897Duval County, Florida 143.2 593883El Paso County, Texas 128.1 490295Erie County, New York 148.7 633744Essex County, Massachusetts 150.6 521910Essex County, New Jersey 144.0 522720Fairfax County, Virginia 172.1 767670Fairfield County, Connecticut 157.6 635155Franklin County, Ohio 151.2 795441Fresno County, California 127.8 568993Fulton County, Georgia 161.7 648216Gwinnett County, Georgia 144.8 539461Hamilton County, Ohio 149.1 537170Harris County, Texas 138.7 2717715Hartford County, Connecticut 150.1 619462Hennepin County, Minnesota 159.3 821205Hidalgo County, Texas 116.2 448031Hillsborough County, Florida 145.4 862369Honolulu County, Hawaii 148.0 666172Hudson County, New Jersey 145.4 464947Jackson County, Missouri 144.0 455571Jefferson County, Alabama 146.6 445156Jefferson County, Kentucky 146.7 516469Kern County, California 123.5 510663King County, Washington 161.2 1432657Kings County, New York 141.0 1733746Lake County, Illinois 155.1 451784Lee County, Florida 140.1 486827Los Angeles County, California 137.2 6623876

County Name (100 Largest Counties) 2013 CHCI 2013 PopulationMacomb County, Michigan 139.3 591956Maricopa County, Arizona 144.0 2602620Marion County, Indiana 141.4 604970Mecklenburg County, North Carolina 153.5 651240Miami-Dade County, Florida 137.1 1821526Middlesex County, Massachusetts 165.9 1082311Middlesex County, New Jersey 152.2 562819Milwaukee County, Wisconsin 143.7 620964Monmouth County, New Jersey 156.3 434984Monroe County, New York 152.6 504245Montgomery County, Maryland 169.9 699058Montgomery County, Pennsylvania 161.5 567631Multnomah County, Oregon 155.5 546304Nassau County, New York 156.2 930419New Haven County, Connecticut 148.6 591400New York County, New York 168.4 1230210Norfolk County, Massachusetts 165.0 478688Oakland County, Michigan 159.2 855070Oklahoma County, Oklahoma 142.5 488639Orange County, California 147.1 2071353Orange County, Florida 145.0 799016Palm Beach County, Florida 145.9 989594Philadelphia County, Pennsylvania 138.1 1023920Pierce County, Washington 142.5 540845Pima County, Arizona 145.2 655656Pinellas County, Florida 144.3 700483Prince George's County, Maryland 143.0 590099Queens County, New York 139.7 1615683Riverside County, California 133.4 1433816Sacramento County, California 142.1 957553Salt Lake County, Utah 147.4 663876San Bernardino County, California 131.0 1270160San Diego County, California 147.0 2118440San Francisco County, California 161.9 658387San Joaquin County, California 127.8 431665San Mateo County, California 157.6 529331Santa Clara County, California 158.8 1262077Shelby County, Tennessee 144.0 601183Snohomish County, Washington 146.2 506228St. Louis County, Missouri 157.9 686741Suffolk County, Massachusetts 151.7 510729Suffolk County, New York 149.1 1021922Tarrant County, Texas 142.6 1207439Travis County, Texas 156.3 741747Ventura County, California 142.6 548617Wake County, North Carolina 161.2 635054Wayne County, Michigan 137.5 1167121Westchester County, New York 159.0 660280Will County, Illinois 146.7 435104Worcester County, Massachusetts 148.9 548851

Appendix B 2013 City Human Capital Index for the 10 Largest Counties in the U.S.




Zip Code CHCI '09 CHCI '10 CHCI '11 Audl t Pop '09 Audl t Pop '10 Audl t Pop '11 CHCI Change '09-'11 Pop Change '09-'1190001 92.4 92.3 92.7 29210 28733 29401 0.3 19190002 99.9 100.1 100.2 23414 24627 25501 0.3 208790003 98.7 99.7 100.4 33790 35177 34385 1.7 59590004 132.3 134.2 134.0 43639 43589 43721 1.8 8290005 125.6 125.3 125.5 28357 27376 27262 -0.1 -109590006 106.3 107.7 107.9 36776 38046 39022 1.6 224690007 112.9 114.5 118.1 19096 19600 19534 5.1 43890008 138.6 138.4 139.5 22856 22905 22901 0.8 4590010 154.5 153.8 158.4 2761 3325 3499 3.9 73890011 89.8 89.8 89.2 54310 54694 54474 -0.6 16490012 120.6 121.2 125.5 21671 21833 21973 4.9 30290013 136.7 139.7 141.0 7147 8231 9170 4.3 202390014 126.2 135.0 140.6 4835 5247 5615 14.3 78090015 111.8 115.0 119.1 11134 11471 11841 7.3 70790016 120.0 122.4 123.1 29375 29816 30557 3.2 118290017 106.4 109.6 109.3 13364 13931 14791 2.9 142790018 114.0 115.1 115.3 30233 30926 31091 1.3 85890019 134.7 134.4 135.4 44099 44848 45433 0.7 133490020 144.6 142.7 141.6 28848 28717 28254 -3.0 -59490021 123.3 118.8 122.4 2263 2356 2597 -0.9 33490022 97.6 97.4 98.4 38805 39199 40761 0.8 195690023 94.5 95.8 96.3 24945 25838 25700 1.8 75590024 182.6 182.3 183.1 22988 22950 23074 0.5 8690025 173.6 174.5 176.1 32821 33003 33842 2.5 102190026 133.2 133.2 134.8 47814 47984 47768 1.6 -4690027 154.3 155.5 156.6 36218 36412 36772 2.3 55490028 146.9 145.0 148.5 20460 20708 20537 1.7 7790029 123.8 123.6 124.8 26821 27258 27753 0.9 93290031 106.9 106.9 108.1 24965 25321 25673 1.2 70890032 115.0 115.2 116.3 28893 29751 29655 1.4 76290033 99.1 99.2 99.8 26961 26887 27067 0.6 10690034 160.5 160.7 161.7 43363 43293 42829 1.2 -53490035 169.2 169.5 169.9 22035 22792 22809 0.7 77490036 177.7 177.0 176.1 27507 27381 27651 -1.6 14490037 97.7 98.0 98.0 34017 34569 35294 0.4 127790038 128.6 129.2 130.0 20441 20300 19874 1.3 -56790039 150.1 151.7 153.4 21373 21038 20655 3.3 -71890040 102.7 103.2 104.2 7615 7610 7778 1.5 16390041 147.7 150.4 153.2 19375 19538 19406 5.5 3190042 128.3 127.6 129.1 42363 41183 40711 0.8 -165290043 133.7 134.6 135.4 28817 28979 29049 1.7 23290044 109.0 110.2 110.4 48342 48477 50501 1.4 215990045 165.7 166.2 166.9 25852 26107 26193 1.3 34190046 167.7 167.2 167.9 40849 41757 42297 0.3 144890047 126.2 126.0 127.7 30200 30707 30600 1.5 40090048 174.5 174.9 175.3 17295 17187 17492 0.8 19790049 185.9 187.7 188.7 27929 27471 27035 2.9 -89490056 172.6 173.7 173.1 6156 6527 6232 0.5 7690057 110.8 110.1 109.3 28475 29292 30323 -1.6 184890058 90.0 89.2 84.7 1756 1632 1591 -5.3 -165

Appendix C CHCI and Population Levels and Changes from 2009 to 2011 in Zip Codes in Los Angeles County




Zip Code CHCI '09 CHCI '10 CHCI '11 Audl t Population '09Audl t Pop '10 Audl t Pop '11 CHCI Change '09-'11 Population Change '09-'1190059 104.2 105.6 107.0 20081 20325 21536 2.8 145590061 106.6 107.5 107.0 14699 14546 14849 0.3 15090062 110.2 108.4 108.7 19059 19888 20454 -1.5 139590063 97.9 98.3 96.5 30630 31393 31292 -1.3 66290064 173.2 174.4 175.9 18511 18740 18950 2.6 43990065 131.5 132.4 131.8 31227 30820 30750 0.3 -47790066 158.6 159.3 158.5 41335 41081 43200 0.0 186590067 171.7 164.2 167.1 2185 1994 2070 -4.7 -11590068 175.5 174.6 173.7 18221 18267 17905 -1.7 -31690069 172.3 171.6 171.9 18097 18574 18782 -0.4 68590073 119.4 116.9 122.9 523 585 739 3.5 21690077 188.8 190.5 190.2 6128 5928 6052 1.4 -7690094 156.5 161.3 167.7 215 161 100 11.2 -11590201 94.1 95.2 95.8 54705 54796 55981 1.7 127690210 172.9 172.0 171.6 15739 15796 15761 -1.3 2290211 161.7 164.7 165.8 5548 5693 5664 4.1 11690212 181.9 180.9 181.7 8601 8823 9028 -0.2 42790220 111.7 112.9 112.7 27586 27433 27756 1.0 17090221 100.9 102.0 102.2 27256 27465 27994 1.3 73890222 106.2 107.6 106.6 16661 16496 17060 0.4 39990230 156.4 156.5 157.2 22717 22981 22845 0.8 12890232 161.6 161.4 159.6 11157 11276 11432 -2.0 27590240 134.2 132.9 134.5 16718 16750 16600 0.3 -11890241 126.8 128.9 131.3 27406 27777 28021 4.6 61590242 125.0 126.3 126.2 25809 25763 25833 1.2 2490245 163.4 163.4 163.2 11776 11704 11634 -0.1 -14290247 128.5 130.3 130.9 30602 31005 31728 2.4 112690248 130.8 130.6 132.2 7383 7071 6998 1.4 -38590249 134.0 132.5 132.5 17858 17941 17561 -1.5 -29790250 126.4 127.1 127.1 57070 58508 58487 0.7 141790254 181.5 180.4 181.5 15065 15049 15095 0.0 3090255 97.5 97.7 97.5 42609 42794 43344 -0.1 73590260 126.0 125.4 124.7 20764 21028 21699 -1.3 93590262 100.3 101.1 102.2 37901 37869 38125 1.8 22490263 225.1 190.2 184.4 148 191 144 -40.7 -490265 177.4 177.3 177.6 12383 12388 12417 0.2 3490266 185.2 184.3 186.1 24253 24746 25131 0.9 87890270 90.5 92.6 93.5 14667 14626 14597 3.0 -7090272 189.0 190.5 191.2 16370 16318 16115 2.1 -25590274 185.8 186.1 186.8 18326 18228 18132 1.0 -19490275 179.7 181.0 179.3 29841 30080 30625 -0.4 78490277 170.9 172.7 174.5 26741 26784 26886 3.7 14590278 169.1 169.2 168.3 28791 29365 28964 -0.8 17390280 101.3 102.5 101.8 53813 55123 55816 0.5 200390290 175.1 174.3 175.4 4893 4865 4900 0.2 790291 170.1 169.8 170.2 21340 21307 21489 0.1 14990292 181.6 180.9 179.9 18398 18472 17920 -1.7 -47890293 177.7 179.2 176.0 10829 10375 10563 -1.7 -26690301 118.6 119.3 117.7 22884 23435 23359 -0.9 47590302 129.3 129.4 130.5 18761 18742 19405 1.3 644





Zip Code CHCI '09 CHCI '10 CHCI '11 Audl t Population '09Audl t Pop '10 Audl t Pop '11 CHCI Change '09-'11 Population Change '09-'1190303 113.1 114.4 113.3 15119 15075 14896 0.1 -22390304 99.4 101.2 100.7 15060 15465 15715 1.3 65590305 150.1 149.9 148.5 10116 10486 10512 -1.6 39690401 170.0 170.3 171.3 5730 5927 6011 1.3 28190402 193.0 193.1 192.2 9582 9430 9051 -0.8 -53190403 182.7 184.0 185.3 19942 19585 19869 2.6 -7390404 160.5 161.8 162.9 15316 15644 15903 2.3 58790405 178.1 178.5 177.2 21562 21762 21909 -1.0 34790501 139.8 139.0 138.4 26807 27578 27712 -1.4 90590502 138.1 142.5 141.6 12133 12166 12423 3.5 29090503 161.3 162.5 163.2 32774 32599 32619 2.0 -15590504 146.4 148.0 147.9 22260 22216 22534 1.4 27490505 163.0 162.2 162.3 25486 26117 26348 -0.7 86290601 138.3 139.2 139.3 21680 21538 22001 1.0 32190602 127.7 128.4 128.8 14889 15186 15355 1.2 46690603 146.8 148.5 148.1 12663 13071 13075 1.3 41290604 129.0 129.6 131.7 25145 25118 25346 2.7 20190605 123.1 124.4 124.7 24736 25496 26238 1.6 150290606 116.5 117.2 117.0 20905 20843 20918 0.6 1390631 135.5 135.2 135.4 42638 42931 43745 -0.1 110790638 142.1 142.3 144.0 30771 31411 31813 1.9 104290640 123.8 123.1 123.4 41126 41221 42021 -0.3 89590650 122.5 122.4 123.0 64907 65076 65404 0.5 49790660 114.9 116.3 116.2 39514 39744 40573 1.3 105990670 118.5 120.5 122.1 9467 9572 9963 3.6 49690701 129.9 131.6 133.5 10996 11098 11021 3.6 2590703 160.3 160.0 161.7 34326 34467 34906 1.4 58090704 126.7 128.8 128.8 2774 2608 2476 2.1 -29890706 127.3 125.9 126.8 45674 45608 46700 -0.5 102690710 133.1 133.9 136.0 17862 17865 17573 3.0 -28990712 144.8 145.3 144.0 20616 20685 20999 -0.8 38390713 144.2 143.6 144.0 18438 18486 18563 -0.2 12590715 135.2 134.8 134.3 13092 13529 13472 -0.8 38090716 106.8 109.5 109.1 8192 8455 8517 2.3 32590717 140.0 139.8 139.8 14521 14848 14820 -0.2 29990723 107.4 106.5 107.8 30026 30197 30484 0.3 45890731 129.7 130.7 130.6 39035 38470 40129 0.9 109490732 152.0 151.0 149.4 15323 15215 14942 -2.6 -38190742 154.3 156.0 163.5 925 827 776 9.2 -14990744 105.2 106.1 106.0 30796 31471 31311 0.8 51590745 132.7 131.0 130.6 37244 37674 37746 -2.1 50290746 143.1 142.5 143.2 17848 18157 18738 0.2 89090802 151.3 148.5 147.8 7499 7390 7298 -3.5 -20190803 145.2 146.2 147.1 28253 28141 28744 1.9 49190804 174.0 174.3 174.8 24341 24714 24686 0.7 34590805 132.1 133.4 134.7 24150 24372 24712 2.7 56290806 118.3 117.5 118.3 51861 52261 53880 0.0 201990807 122.6 123.0 122.8 24014 24408 24890 0.2 87690808 152.4 152.6 151.9 22113 22769 23348 -0.5 123590810 156.1 155.6 156.3 27440 27194 27865 0.2 425





Zip Code CHCI '09 CHCI '10 CHCI '11 Audl t Population '09Audl t Pop '10 Audl t Pop '11 CHCI Change '09-'11 Population Change '09-'1190813 121.0 120.4 122.5 21858 21612 21974 1.5 11690814 107.0 106.7 107.4 30767 32068 32744 0.5 197790815 163.8 165.1 164.0 13942 14067 14344 0.2 40290822 159.3 159.1 159.0 26441 26039 26404 -0.3 -3791001 144.6 137.1 133.8 29 164 154 -10.8 12591007 155.0 155.7 157.2 26172 26266 26101 2.2 -7191008 160.9 161.3 161.6 22474 22472 22407 0.7 -6791010 161.7 160.7 160.7 23842 24168 24116 -1.1 27491011 157.1 161.1 164.8 955 899 980 7.7 2591016 131.8 132.2 133.6 16946 17199 17289 1.7 34391020 184.7 184.7 188.0 13507 13499 13308 3.3 -19991024 144.6 146.1 146.8 27947 27864 28622 2.2 67591030 160.5 157.1 152.2 5963 5933 5953 -8.3 -1091040 141.8 141.8 142.2 14500 14283 14418 0.4 -8291042 140.3 139.0 137.2 19818 20465 20879 -3.1 106191101 167.9 169.2 169.3 15653 16044 16122 1.4 46991103 132.1 132.4 136.3 18490 18142 17969 4.2 -52191104 144.0 146.4 147.1 26102 26047 26515 3.1 41391105 184.7 185.3 186.1 8616 8899 9435 1.3 81991106 163.7 166.1 167.6 17621 17512 17413 3.9 -20891107 164.1 163.9 163.4 23212 23702 24169 -0.7 95791108 187.9 187.5 185.0 9411 9397 9530 -2.8 11991201 136.9 136.8 138.4 17206 17074 17094 1.5 -11291202 153.5 154.0 153.2 16813 17019 17099 -0.2 28691203 138.1 139.8 141.1 9226 9618 9493 3.0 26791204 133.9 132.2 131.7 11204 11681 11436 -2.2 23291205 138.2 136.8 134.6 27245 26765 26842 -3.6 -40391206 152.0 150.2 151.2 24576 24365 25203 -0.8 62791207 167.7 168.4 163.5 7786 7901 8063 -4.2 27791208 168.7 167.9 165.3 10972 11039 11275 -3.4 30391210 175.2 179.1 164.2 146 168 328 -11.0 18291214 162.9 162.3 164.3 20908 21222 21254 1.4 34691301 169.4 167.4 168.5 17341 17955 18432 -0.9 109191302 180.0 178.9 177.9 16896 16690 16903 -2.1 791303 124.1 126.6 127.0 16034 16276 16881 2.9 84791304 136.4 137.6 137.6 34284 34945 34885 1.1 60191306 130.8 131.3 129.6 30427 30655 31722 -1.2 129591307 159.8 161.1 162.0 17138 17458 17853 2.2 71591311 152.3 153.2 153.1 26966 27249 27426 0.8 46091316 162.4 164.4 162.9 20217 20310 21347 0.4 113091321 130.7 133.0 132.3 21873 21806 21992 1.6 11991324 147.4 145.6 145.3 16854 16937 17314 -2.1 46091325 150.3 150.1 150.7 20918 21297 21335 0.3 41791326 164.5 165.0 165.5 24296 25042 25351 1.0 105591330 156.9 166.9 169.3 79 200 247 12.5 16891331 101.0 103.2 103.2 56150 56821 59394 2.2 324491335 129.3 129.3 128.8 48025 48304 49747 -0.5 172291340 104.7 106.8 109.1 19306 19849 20866 4.4 156091342 120.5 121.5 121.1 52810 53230 54574 0.7 176491343 128.3 127.4 127.1 36719 37431 38536 -1.2 1817





Zip Code CHCI '09 CHCI '10 CHCI '11 Audl t Population '09Audl t Pop '10 Audl t Pop '11 CHCI Change '09-'11 Population Change '09-'1191344 147.8 147.8 147.7 36603 36402 36675 0.0 7291345 129.1 129.3 127.8 12279 12337 12464 -1.3 18591350 150.6 151.2 150.1 20429 21470 22670 -0.5 224191351 136.8 135.4 135.9 19863 20286 20438 -0.9 57591352 116.0 115.8 115.9 28597 28089 28978 -0.1 38191354 159.4 162.0 164.9 17942 17998 18533 5.5 59191355 156.3 156.0 156.5 21362 22396 22029 0.2 66791356 157.3 157.7 158.5 21668 21182 21651 1.1 -1791361 173.9 173.6 173.9 14422 14331 14873 0.0 45191362 169.1 167.9 167.4 23962 24347 24317 -1.7 35591364 167.1 165.8 165.9 18353 18866 18926 -1.2 57391367 165.9 166.5 166.1 28509 29214 29244 0.3 73591377 177.0 174.8 176.3 9058 9402 9460 -0.8 40291381 166.3 168.3 169.1 11247 11701 11823 2.8 57691384 136.8 140.4 137.4 18042 17759 17995 0.6 -4791387 145.3 144.6 144.9 24831 25587 25988 -0.4 115791390 152.8 154.1 151.6 12535 13017 13275 -1.2 74091401 135.2 137.1 137.7 26689 27272 27116 2.5 42791402 112.7 112.8 114.2 40981 41481 42193 1.5 121291403 172.1 172.3 172.5 18463 18488 18673 0.5 21091405 124.2 124.6 123.1 32304 33343 33895 -1.1 159191406 130.1 129.0 130.1 34274 34626 34191 -0.1 -8391411 132.3 134.8 133.9 16271 16462 16835 1.6 56491423 166.7 168.2 168.1 23631 23529 23915 1.4 28491436 177.9 176.2 176.4 10200 10418 10496 -1.5 29691501 148.4 148.3 148.9 15278 14895 14869 0.5 -40991502 134.5 133.6 133.7 8353 8468 8586 -0.8 23391504 146.8 149.1 149.1 18197 17965 17906 2.3 -29191505 152.2 153.4 153.2 21102 21743 22714 1.0 161291506 151.0 151.0 151.5 13626 13802 13402 0.5 -22491601 143.5 145.7 144.5 24730 25822 26520 1.0 179091602 162.0 165.7 166.7 14190 13932 14226 4.7 3691604 172.4 172.2 172.4 21861 22391 22419 0.0 55891605 115.8 117.7 118.5 33987 35163 35209 2.7 122291606 125.3 125.5 125.9 30390 30726 29950 0.6 -44091607 157.0 158.6 157.3 21657 21888 21426 0.4 -23191702 124.6 122.8 123.0 33394 33487 33041 -1.6 -35391706 110.8 109.7 110.4 44848 45350 46215 -0.4 136791711 171.7 172.8 170.7 21974 22141 22439 -1.0 46591722 128.6 129.7 129.7 22132 22524 22880 1.1 74891723 137.2 137.6 136.0 11979 11801 12339 -1.3 36091724 142.2 142.4 142.2 15933 16074 16280 0.0 34791731 107.7 109.3 109.6 18407 19086 19518 2.0 111191732 108.6 110.5 112.2 37816 38031 39059 3.6 124391733 100.6 101.4 101.5 26537 26869 27194 0.9 65791740 137.0 138.3 139.2 16659 17127 17529 2.2 87091741 154.2 155.5 156.2 16503 16204 16651 2.0 14891744 110.7 112.0 111.0 47843 49121 50473 0.3 263091745 143.7 145.8 145.2 38330 38964 38643 1.4 31391746 112.6 111.9 110.5 19266 18857 18038 -2.0 -1228





Zip Code CHCI '09 CHCI '10 CHCI '11 Audl t Population '09Audl t Pop '10 Audl t Pop '11 CHCI Change '09-'11 Population Change '09-'1191748 145.4 144.6 145.3 32742 32677 33381 -0.1 63991750 149.9 149.9 151.8 22635 22897 22869 1.9 23491754 136.9 135.5 137.7 23059 22792 23831 0.9 77291755 130.6 131.4 130.9 20146 20358 20514 0.3 36891765 162.4 162.7 160.9 32745 33368 33078 -1.5 33391766 116.5 117.1 117.4 38959 39123 40274 0.9 131591767 121.9 121.5 122.9 28399 29412 29065 1.0 66691768 116.0 116.6 118.3 18779 18885 19675 2.3 89691770 117.1 116.8 117.0 43077 43109 44080 -0.1 100391773 149.9 150.6 150.6 23089 23082 23221 0.8 13291775 154.1 154.0 156.3 17240 17331 18064 2.2 82491776 132.1 130.3 129.4 27101 27396 27387 -2.8 28691780 147.0 146.6 147.8 23902 24548 24676 0.7 77491789 160.1 160.0 161.7 28650 28955 29239 1.6 58991790 130.0 131.0 131.7 27867 27459 29002 1.7 113591791 142.5 142.4 142.7 20906 21185 21058 0.2 15291792 144.0 142.4 144.1 20027 20415 20268 0.1 24191801 140.9 140.9 142.3 38712 39203 39026 1.4 31491803 134.7 135.6 138.5 21892 21722 21565 3.9 -32793510 143.0 146.3 146.1 11556 12032 11866 3.1 31093532 152.8 153.7 153.2 12339 12811 13312 0.4 97393534 129.1 126.0 121.4 726 800 764 -7.6 3893535 134.9 134.1 132.2 2163 2295 2447 -2.7 28493536 137.8 136.9 136.0 46851 47985 50386 -1.8 353593543 123.4 124.7 123.2 48722 49991 50176 -0.2 145493544 150.9 153.0 152.2 24150 24253 24570 1.4 42093550 158.3 158.3 161.0 9820 9431 10567 2.7 74793551 157.6 157.1 157.1 13804 14718 15221 -0.5 141793552 142.4 140.5 140.0 1043 998 1053 -2.4 1093553 107.9 133.4 126.8 314 319 510 18.9 19693563 127.2 131.0 131.4 272 248 283 4.3 1193591 129.0 129.1 131.0 15616 15844 16008 2.0 392





Rank Geography 2013 CHCI Adult Population1 Palo Alto USD 199.6 531852 Davis Joint USD 186.5 383243 Berkeley USD 181.0 747634 Santa Monica-Malibu USD 178.3 863805 San Ramon Valley USD 178.2 1001616 Las Virgenes USD 176.2 444887 Irvine USD 176.0 1291128 Pleasanton USD 174.1 499859 Redondo Beach USD 170.2 50545

10 Poway USD 169.2 12676611 Santa Clara USD 167.5 10520112 Fremont USD 167.2 15455613 Capistrano USD 165.1 24104714 Conejo Valley USD 164.0 9483115 Carlsbad USD 162.4 5348116 San Francisco USD 161.9 65838717 Pasadena USD 161.7 14914418 Newport-Mesa USD 161.6 14098019 Torrance USD 159.1 10485320 Alameda City USD 157.7 5670321 Saddleback Valley USD 157.3 15056422 Tustin USD 155.4 9426623 Livermore Valley Joint USD 155.4 5880024 Redlands USD 155.3 8056625 San Luis Coastal USD 155.2 5311126 Santa Barbara USD 153.8 5678827 Milpitas USD 153.3 4800228 San Diego City USD 153.3 70313329 Placentia-Yorba Linda USD 153.0 11324530 Chico USD 152.6 6725731 Folsom-Cordova USD 152.5 8564132 San Jose USD 152.0 18672233 Mount Diablo USD 151.5 18048934 Clovis USD 151.1 12751935 Burbank USD 150.8 7789336 Natomas USD 150.4 4857537 Monterey Peninsula USD 150.2 5883538 Upland USD 148.9 5121639 Glendale USD 148.9 15915740 San Juan USD 148.4 22974141 Ventura USD 148.3 8325642 Oakland USD 147.7 28548843 Murrieta Valley USD 146.8 6545344 Orange USD 146.5 15088545 Simi Valley USD 146.1 9070246 Temecula Valley USD 145.4 9085647 ABC USD 145.1 7434348 Alhambra USD 144.7 8646449 South San Francisco USD 144.3 5551750 New Haven USD 143.6 53873

Rank Geography 2013 CHCI Adult Population51 Sacramento City USD 142.5 22133252 Chino Valley USD 142.2 11202153 Lucia Mar USD 141.7 5630954 San Leandro USD 141.5 5117655 San Marcos USD 141.2 8172256 Vacaville USD 141.2 5163357 Riverside USD 141.1 15058058 Desert Sands USD 140.7 12499159 Napa Valley USD 140.5 7988660 Corona-Norco USD 140.4 16989261 West Contra Costa USD 140.3 16886462 Elk Grove USD 140.3 20060263 Long Beach USD 138.9 33700564 Palm Springs USD 137.9 12000165 Vallejo City USD 137.8 8709866 Rowland USD 137.3 7448267 Fairfield-Suisun USD 137.3 8384968 Oceanside USD 135.6 8960069 West Covina USD 134.9 4396970 Apple Valley USD 134.6 5240171 Vista USD 134.4 10631972 Hayward USD 134.2 11823973 Antioch USD 134.2 6986874 Bellflower USD 133.7 5410475 Los Angeles USD 133.5 307601776 Lodi USD 133.4 10394077 Morongo USD 133.1 4518678 Tracy Joint USD 132.9 4345479 Covina-Valley USD 132.7 5277680 Lake Elsinore USD 132.1 7609481 Yuba City USD 131.7 4589182 Visalia USD 131.0 8765283 Turlock USD 130.8 4556584 Norwalk-La Mirada USD 130.5 7801985 San Lorenzo USD 130.4 5320586 Hacienda La Puente USD 129.9 7429287 Inglewood USD 129.9 7430488 Hemet USD 129.6 9137689 Downey USD 129.6 7524590 Central USD 128.8 4633891 Pajaro Valley Joint USD 127.6 7096892 Manteca USD 126.7 7814493 Twin Rivers USD 126.4 10148994 Fresno USD 125.9 21981095 Hesperia USD 125.1 6045396 Pomona USD 125.0 10435197 Azusa USD 124.7 3765698 Garden Grove USD 124.6 18759199 Alvord USD 124.1 66984

100 Colton Joint USD 123.7 70593

Appendix D 2013 City Human Capital Index for Major Unfiled School Districts in California




Rank Geography 2013 CHCI Adult Population101 Moreno Valley USD 123.6 102817102 Rialto USD 118.7 67491103 Jurupa USD 118.0 58652104 San Bernardino City USD 117.1 149361105 Val Verde USD 116.7 53293106 Fontana USD 116.4 105868107 Stockton USD 114.7 124640108 Montebello USD 114.4 110557109 Paramount USD 113.7 42649110 Compton USD 111.5 93610111 Madera USD 111.3 52871112 Baldwin Park USD 111.2 46962113 Santa Ana USD 110.0 154805114 Coachella Valley USD 109.8 48518115 Lynwood USD 103.0 37378

Appendix D 2013 City Human Capital Index for Major Unfiled School Districts in California


JUNE 2015 REPORT


Charts



15141312111009080706050403

16000

15500

15000

14500

14000

18000

17500

17000

16500

16000

15500

California Employment (6-mo. moving avg.)Jan. 2003 to March 2015 (Thous.)

Wage & Salary Emp. (Left) HH Survey Emp. (Right) 14121008060402009896

14

12

10

8

6

4

(Percent)

California Unemployment RateJan. 1995 to April 2015

1312111009080706050403020100

600

550

500

450

400

(Bil. $)

Taxable Sales in California2000:1Q to 2013:4Q

1514131211100908070605040302

200

150

100

50

0

Indexed 1985 = 100

Source: Conference Board

Indexes of Consumer Attitudes--Pacific AreaJan. 2002 to April 2015

Consumer Confidence Present Expectations



151413121110090807060504

2.0

1.5

1.0

0.5

0.0

(Mil.)

California New Car RegistrationsJan. 2004 to Feb. 2015

(3-mo. moving avg.)

15141312111009080706050403

400

300

200

100

0

(Thous.)

New One-Family Houses SoldWestern Region

Jan. 2003 to March 2015

(3-mo. moving average)

151311090705030199979593918987

600

500

400

300

200

100

(Thous. $)

Source: California Association of Realtors

California Existing-Home Prices1987:Q1 to 2015Q1

151413121110090807060504

700

600

500

400

300

200

(Thous.)

Source: California Association of Realtors

California Existing-Home SalesJan. 2004 to March 2015



141312111009080706050403

30000

25000

20000

15000

10000

5000

(Mil. $)

Building Permit ValuationsTotal Nonresidential

Jan. 2003 to March 2015

3-mo. moving avg.15141312111009080706050403

250

200

150

100

50

0

(Thous.)

New Residential Units ThroughCalifornia Building PermitsJan. 2003 to March 2015

Single-Unit Multi-Unit

15141312111009080706050403

1000

900

800

700

600

500

(Thous.)

California Construction EmploymentJan. 2003 to March 2015

15141312111009080706050403

250024002300220021002000190018001700

14000

13500

13000

12500

12000

(Thous.) (Thous.)

California Employment by SectorJan. 2003 to March 2015

Goods Producing (Left) Services (Right)

CHARTS – FORECAST


201720112005199919931987198119751969

15

10

5

0

-5

-10

(% Change Year Ago)

Real Personal IncomeCalifornia versus U.S.

California U.S.201720112005199919931987198119751969

86420

-2-4-6-8

(% Change Year Ago)

Nonfarm EmploymentCalifornia versus U.S.

California U.S.

2017201120051999199319871981197519691963

14

12

10

8

6

4

2

(Percent)

Rates of UnemploymentCalifornia versus U.S.

California U.S.2017201120051999199319871981197519691963

15

10

5

0

-5

-10

(3-Yr. % Ch.)

California Employment versusReal Personal Income

Nonfarm Emp. Real Personal Income

CHARTS – FORECAST


2017201120051999199319871981197519691963

20

15

10

5

0

-5

(4-Qtr Percent Change)California Consumer Price Inflation

California U.S.

2017201220072002199719921987

24000220002000018000160001400012000100008000

(Thous)

California Nonfarm EmploymentHistory & Forecast

Vs. 2.3% Trend from 1990:3

6.5 Million Jobs Below Trendby Year 2017

History & Forecast 2.3% Trend Line201720112005199919931987198119751969

13

12

11

10

9

8

(Percent)

California Share of U.S.Employment and Population

Emp Population

201720112005199919931987198119751969

20

10

0

-10

-20

-30

(% Change Year Ago)Real California Taxable Sales

CHARTS – FORECAST


2017201120051999199319871981197519691963

500400300200100

0-100-200-300

(Thous.)

California Net Natural Increase andNet Inmigration

Immigration Natural Increase

20172011200519991993198719811975

52

50

48

46

44

42

40

(Percent)

Gross Labor Force Participation RateLabor Force/Total Population

California U.S.2017201120051999199319871981197519691963

40

35

30

25

20

15

(Ca. Mil.; U.S. 10 Mil.)Population of California vs. U.S.

California U.S.

201720112005199919931987198119751969

3.0

2.5

2.0

1.5

1.0

0.5

0.0

(4-Qtr Percent Change)Growth in Population

CHARTS – FORECAST


20172011200519991993198719811975

400

300

200

100

0

(Thous. Units)

New Residential Units ThroughCalifornia Building Permits

Single-Unit Multi-Unit

2017201220072002199719921987

1000

900

800

700

600

500

400

(Thous.)

California Employmentin Construction

201720122007200219971992198719821977

40

35

30

25

20

15

10

5

(Bil. 2009 $)

Real Value of NonresidentialConstruction in California

201720112005199919931987198119751969

350

300

250

200

150

100

50

0

(Thous. $)U.S. Median Price of Single-Family Homes

CHARTS – FORECAST


201720152013201120092007200520032001

1900

1800

1700

1600

1500

1400

1300

1200

(Thous.)

California Employmentin Manufacturing

201720152013201120092007200520032001

2500

2400

2300

2200

2100

(Thous.)

California Employmentin Trade

201720152013201120092007200520032001

600

550

500

450

400

(Thous.)

California Employmentin Information

201720152013201120092007200520032001

2600

2400

2200

2000

1800

1600

1400

(Thous.)

California Employmentin Education and Health Services

CHARTS – FORECAST


201720152013201120092007200520032001

2300

2250

2200

2150

2100

2050

(Thous.)

California Employmentin State and Local Government

201720152013201120092007200520032001

300

290

280

270

260

250

240

230

(Thous.)

California Employmentin Federal Government

201720152013201120092007200520032001

2800

2600

2400

2200

2000

1800

(Thous.)

California Employmentin Professional & Business Services

201720152013201120092007200520032001

950

900

850

800

750

700

(Thous.)

California Employmentin Financial Activities


JUNE 2015 REPORT

Tables



Table 1. Summary of the UCLA Forecast for California 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Personal Income, Taxable Sales, and Price Inflation (%Change)Personal Income (Bil.$) 1564.3 1596.2 1537.1 1578.6 1685.6 1805.2 1856.6 1944.4 2054.0 2198.0 2336.6 Calif. (% Ch) 4.3 2.0 -3.7 2.7 6.8 7.1 2.8 4.7 5.6 7.0 6.3 U.S.(% Ch) 5.3 3.6 -2.8 2.8 6.2 5.2 2.0 4.0 4.1 5.0 6.1Pers. Income (Bil. 2009$) 1604.6 1595.4 1537.1 1558.6 1632.3 1713.8 1741.9 1796.2 1876.3 1959.0 2028.4 Calif. (% Ch) 1.4 -0.6 -3.7 1.4 4.7 5.0 1.6 3.1 4.5 4.4 3.5 U.S. (% Ch) 2.7 0.6 -2.7 1.1 3.7 3.3 0.8 2.6 3.8 2.8 3.5Taxable Sales (Bil.$) 561.3 532.4 456.6 477.0 520.2 558.1 586.5 613.4 640.1 669.0 697.2 (% Ch) 0.3 -5.2 -14.2 4.5 9.1 7.3 5.1 4.6 4.4 4.5 4.2 (Bil. 2009$) 575.8 532.1 456.6 471.0 503.7 529.8 550.3 566.7 584.7 596.3 605.2 (% Ch) -2.5 -7.6 -14.2 3.2 7.0 5.2 3.9 3.0 3.2 2.0 1.5Consumer Prices (% Ch) 3.3 3.4 -0.3 1.3 2.7 2.2 1.5 1.8 0.9 3.0 3.1

Employment and Labor Force (Household Survey, % Change)Employment 0.9 -0.5 -4.0 -0.5 1.0 2.2 2.5 2.2 2.5 2.1 1.3Labor Force 1.4 1.7 0.1 0.5 0.3 0.7 0.8 0.9 1.1 1.0 1.0Unemployment Rate (%) 5.4 7.5 11.2 12.1 11.6 10.2 8.8 7.5 6.2 5.2 5.0 U.S. 4.6 5.8 9.3 9.6 8.9 8.1 7.4 6.2 5.4 5.0 4.9 Total Nonfarm Nonfarm Employment (Payroll Survey, % Change) Calif. 0.8 -1.1 -5.7 -1.1 1.0 2.4 3.2 3.0 2.9 2.1 1.3 U.S. 1.1 -0.6 -4.3 -0.7 1.2 1.7 1.7 1.9 2.1 1.5 1.4Construction -4.4 -11.7 -20.9 -10.2 0.2 5.1 8.0 6.0 6.2 2.9 1.3Manufacturing -1.7 -2.6 -10.0 -3.1 0.5 0.4 0.1 1.1 0.7 1.4 1.0 Nondurable Goods -1.1 -2.0 -8.1 -2.5 -0.4 0.3 0.5 0.5 -0.5 1.4 1.1 Durable Goods -2.0 -3.0 -11.2 -3.4 1.0 0.4 -0.1 1.4 1.4 1.4 0.9Trans. Warehousing & Util 2.3 -0.5 -6.0 -1.8 1.7 2.7 3.2 3.9 3.6 2.0 1.3Trade 1.1 -2.5 -7.5 -0.3 2.0 1.9 2.0 2.5 2.1 1.3 1.0Information 1.1 1.1 -7.3 -2.8 0.4 1.0 3.1 2.1 3.1 2.4 1.2Financial Activities -3.4 -6.1 -7.0 -2.9 0.2 1.5 1.2 0.2 2.3 1.5 1.3Professional Busi. Serv. 1.1 -1.2 -7.9 0.6 2.8 5.0 4.4 3.9 5.0 3.8 1.9Edu. & Health Serv. 3.8 4.0 2.7 0.6 1.4 4.2 7.1 3.8 2.7 2.3 1.3Leisure & Hospitality 2.7 0.8 -4.4 -0.1 2.3 4.1 4.9 4.8 3.8 2.4 1.8Other Services 1.0 -0.2 -4.9 -0.3 1.8 2.2 2.4 4.5 3.2 2.4 1.8Federal Gov’t -0.6 0.5 1.1 6.8 -4.9 -1.9 -2.0 -1.3 0.1 0.8 0.8State & Local Gov’t 2.0 1.0 -1.9 -2.2 -1.4 -1.1 0.2 1.9 1.4 0.9 0.6

Nonfarm Employment (Payroll Survey, Thous.)Total Nonfarm 15414 15246 14378 14216 14365 14711 15183 15645 16098 16437 16649Construction 893 788 624 560 561 590 637 675 717 738 748Manufacturing 1465 1427 1284 1244 1250 1255 1256 1270 1278 1296 1309 Nondurable Goods 536 526 483 471 469 471 473 475 473 480 485 Durable Goods 929 901 801 773 781 784 783 794 805 816 824Trans. Warehousing & Util 508 505 475 466 474 487 503 522 541 552 559Trade 2405 2345 2168 2162 2204 2247 2291 2349 2397 2429 2453Information 471 476 441 429 431 435 449 458 472 484 489Financial Activities 897 842 783 760 762 773 783 784 802 814 824Professional Busi. Serv. 2268 2241 2064 2077 2135 2242 2341 2433 2555 2653 2704Edu. & Health Serv. 1913 1990 2044 2056 2084 2172 2325 2414 2481 2538 2570Leisure & Hospitality 1560 1573 1503 1502 1536 1599 1676 1757 1824 1868 1901Other Services 512.1 511.3 486.2 485.0 493.7 504.7 516.6 539.8 557.1 570.6 580.8Federal Gov’t 247.1 248.4 251.2 268.3 255.2 250.5 245.5 242.2 242.5 244.4 246.2State & Local Gov’t 2247.9 2271.0 2228.2 2179.9 2149.5 2125.5 2128.8 2168.9 2200.1 2220.7 2234.2

Population and MigrationNet Inmigration(Thous) -24 -25 -89 -51 -11 39 45 92 95 129 146Population (Thous) 36553 36856 37077 37307 37556 37842 38136 38458 38794 39143 39511 (% Ch) 0.8 0.8 0.6 0.6 0.7 0.8 0.8 0.8 0.9 0.9 0.9

Construction ActivityResidential Building Permits (Thous. Un.) 106.5 60.8 33.2 43.0 44.9 56.6 77.8 82.7 112.6 128.9 134.1Nonres.Permits (Mil.’09$) 23176 18819 10895 11303 12818 13890 20115 20888 21023 21982 23158



Table 2. Quarterly Summary of the UCLA Forecast for California 2015:1 2015:2 2015:3 2015:4 2016:1 2016:2 2016:3 2016:4 2017:1 2017:2 2017:3 2017:4

Personal Income, Taxable Sales, and Price Inflation (%Change)Personal Income (Bil.$) 2007.5 2031.9 2068.6 2108.2 2146.8 2181.9 2214.9 2248.6 2287.3 2320.9 2352.7 2385.7 Calif.(% Ch) 5.6 5.0 7.4 7.9 7.5 6.7 6.2 6.2 7.1 6.0 5.6 5.7 U.S. (% Ch) 4.0 3.8 3.9 4.5 5.7 5.2 5.5 6.2 7.0 5.9 5.8 5.7Pers. Income (Bil. 2009$) 1844.7 1863.2 1886.5 1910.8 1933.1 1951.7 1967.7 1983.3 2003.9 2020.9 2036.0 2052.9 Calif.(% Ch) 5.3 4.1 5.1 5.2 4.8 3.9 3.3 3.2 4.2 3.4 3.0 3.4 U.S. (% Ch) 6.1 2.4 2.0 2.7 3.4 2.6 2.8 3.4 4.3 3.5 3.3 3.2Taxable Sales (Bil. $) 629.6 636.2 643.7 650.9 658.1 665.6 672.6 679.9 686.8 693.4 700.7 707.9 (% Ch) 4.3 4.3 4.8 4.6 4.5 4.6 4.3 4.4 4.1 3.9 4.2 4.2 (Bil. 2009$) 578.5 583.4 587.0 590.0 592.6 595.3 597.5 599.7 601.7 603.8 606.4 609.1 (%Ch) 4.0 3.4 2.5 2.0 1.8 1.9 1.5 1.5 1.4 1.4 1.7 1.8Consumer Prices (% Ch) -0.8 0.9 3.1 2.9 3.2 3.0 3.5 3.5 3.0 2.9 2.9 2.8

Employment and Labor Force (Household Survey, % Change)Employment 2.9 2.5 2.2 2.4 2.1 2.1 1.7 1.6 1.2 1.0 0.9 0.9Labor Force 0.8 0.8 1.0 1.0 1.2 1.0 1.0 1.0 1.1 1.0 1.0 1.0Unemployment Rate (%) 6.7 6.3 6.0 5.7 5.5 5.3 5.1 5.0 4.9 4.9 5.0 5.0 U.S. 5.6 5.4 5.3 5.2 5.1 5.0 4.9 4.8 4.8 4.8 4.9 4.9 Total Nonfarm Nonfarm Employment (Payroll Survey, % Change) Calif. 3.4 2.4 2.3 2.4 2.2 2.1 1.5 1.4 1.3 1.1 1.0 0.9 U.S. 2.2 1.8 1.8 1.5 1.4 1.6 1.4 1.4 1.4 1.3 1.2 1.0Construction 10.1 3.9 4.5 4.5 2.2 2.1 1.8 1.5 1.1 1.0 1.0 0.8Manufacturing 0.1 1.3 1.6 1.4 1.5 1.4 1.3 1.5 0.7 0.6 0.7 1.2 Nondurable Goods 0.1 1.4 1.8 1.6 1.3 1.7 1.0 0.9 0.9 0.9 1.4 2.2 Durable Goods 0.1 1.3 1.5 1.3 1.6 1.2 1.4 1.8 0.6 0.5 0.4 0.7Trans. Warehousing & Util. 1.1 2.3 2.2 2.4 2.2 2.1 1.1 0.9 1.3 1.5 1.5 1.3Trade 2.8 1.2 1.1 1.1 1.5 1.7 1.0 1.0 1.3 0.8 0.7 0.7Information 4.3 2.3 2.4 3.2 2.7 3.0 0.9 0.9 1.1 1.2 1.1 1.1Financial Activities 5.3 1.3 1.1 1.3 1.6 1.7 1.6 1.5 1.1 1.1 1.1 1.0Professional Busi. Serv. 6.6 4.7 4.0 4.1 4.4 4.3 2.1 1.8 1.9 1.6 1.5 1.4Edu. & Health Serv. 2.6 2.9 2.5 2.8 2.4 1.9 1.8 1.6 1.3 0.9 0.7 0.7Leisure & Hospitality 5.7 2.3 2.9 2.9 2.1 2.1 2.2 2.1 1.9 1.4 1.4 1.2Other Services 3.2 3.4 2.3 2.7 2.3 3.0 1.6 1.6 1.7 1.7 1.7 1.8Federal Gov’t -1.6 1.1 0.8 0.8 0.8 0.8 0.7 0.8 0.9 1.0 0.5 -0.1State and Local Gov’t 0.1 1.3 1.1 1.3 0.9 0.8 0.6 0.6 0.7 0.6 0.5 0.5

Nonfarm Employment (Payroll Survey, Thous.)Total Nonfarm 15957 16053 16143 16240 16328 16415 16475 16531 16586 16630 16670 16709Construction 706 713 721 729 733 737 740 743 745 747 749 750Manufacturing 1271 1275 1281 1285 1290 1294 1298 1303 1305 1307 1310 1314 Nondurable Goods 470 472 474 476 477 479 481 482 483 484 486 488 Durable Goods 801 803 807 809 812 815 818 821 823 824 824 826Trans. Warehousing & Util. 536 539 542 546 548 551 553 554 556 558 560 562Trade 2387 2394 2401 2408 2417 2427 2433 2439 2447 2451 2456 2460Information 468 471 473 477 480 484 485 486 487 489 490 491Financial Activities 798 801 803 806 809 813 816 819 821 823 826 827Professional Busi. Serv. 2515 2544 2568 2594 2623 2650 2664 2676 2689 2700 2710 2719Edu. & Health Serv. 2455.2 2473.0 2488.5 2505.9 2520.6 2532.6 2543.7 2553.7 2562.1 2567.6 2572.0 2576.4Leisure & Hospitality 1806.5 1816.8 1829.8 1842.9 1852.6 1862.4 1872.7 1882.6 1891.3 1897.9 1904.3 1909.8Other Services 551.1 555.8 558.9 562.6 565.7 569.9 572.2 574.6 577.0 579.5 582.0 584.5Federal Gov’t 242 242 243 243 244 244 245 245 246 246 247 246State and Local Gov’t 2190 2197 2203 2210 2215 2219 2222 2226 2230 2233 2236 2238

Population and MigrationNet Inmigration(Thous) 81.8 91.8 99.5 108.1 117.6 125.9 133.9 137.2 140.5 144.6 148.4 151.5Population (Thous) 38668 38751 38836 38922 39009 39097 39187 39278 39370 39463 39557 39653 (% Ch) 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 1.0 1.0 1.0

Construction ActivityResidential Building Permits (Thous. Units) 107.1 109.2 113.7 120.5 122.0 129.0 130.5 134.0 133.1 135.3 134.4 133.7Nonres.Permits (Mil. ‘09$) 20912 21024 21059 21099 21275 21579 22207 22864 23131 23195 23199 23108



Table 3. Personal Income, Taxable Sales, Construction and Population in California 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Aggregates (Bil $)Personal Income 1564.3 1596.2 1537.1 1578.6 1685.6 1805.2 1856.6 1944.4 2054.0 2198.0 2336.6Disposable Income 1348.9 1395.3 1375.7 1405.7 1483.3 1586.5 1604.8 1679.3 1763.3 1879.7 1994.0 (Bil 2009$)Personal Income 1604.6 1595.4 1537.1 1558.6 1632.3 1713.8 1741.9 1796.2 1876.3 1959.0 2028.4Disposable Income 1383.7 1394.5 1375.8 1387.9 1436.4 1506.2 1505.7 1551.3 1610.7 1675.2 1731.0 (Nominal %Ch)Personal Income 4.3 2.0 -3.7 2.7 6.8 7.1 2.8 4.7 5.6 7.0 6.3Disposable Income 3.6 3.4 -1.4 2.2 5.5 7.0 1.2 4.6 5.0 6.6 6.1 (Real %Ch)Personal Income 1.4 -0.6 -3.7 1.4 4.7 5.0 1.6 3.1 4.5 4.4 3.5Disposable Income 0.7 0.8 -1.3 0.9 3.5 4.9 -0.0 3.0 3.8 4.0 3.3

Components of Personal Income (Bil $)Personal Income 1564.3 1596.2 1537.1 1578.6 1685.6 1805.2 1856.6 1944.4 2054.0 2198.0 2336.6 Wages & Salaries 834.4 842.9 799.5 814.5 848.7 900.5 934.7 989.0 1043.3 1106.0 1167.2 Other Labor Income 200.6 204.8 197.1 203.3 218.9 219.2 227.9 238.9 252.9 277.8 301.6 Farm 7.5 5.2 5.7 6.3 10.6 10.1 9.8 8.8 15.0 25.0 34.6 Other Income 452.6 458.0 415.9 415.4 460.5 525.0 543.5 564.8 595.3 635.6 674.8 Transfer Payments 192.1 210.3 239.9 261.0 261.8 269.7 281.2 292.7 307.9 326.9 342.9 Social Insurance 122.7 124.9 121.0 121.9 114.5 119.0 140.1 149.3 159.8 172.8 184.1

Taxable Sales NominalLevel (Bil $) 561.3 532.4 456.6 477.0 520.2 558.1 586.5 613.4 640.1 669.0 697.2 %Ch 0.3 -5.2 -14.2 4.5 9.1 7.3 5.1 4.6 4.4 4.5 4.2 RealLevel (Bil. 2009$) 575.8 532.1 456.6 471.0 503.7 529.8 550.3 566.7 584.7 596.3 605.2 %Ch -2.5 -7.6 -14.2 3.2 7.0 5.2 3.9 3.0 3.2 2.0 1.5

New Automobile Sales (Mil Un.)New Registrations 1.68 1.34 0.99 1.11 1.21 1.52 1.68 1.80 1.89 1.93 1.95U.S. Sales 16.09 13.19 10.40 11.55 12.74 14.43 15.52 16.40 16.91 17.20 17.49

Construction Activity Residential Building Permits (Thous.)Total 106.5 60.8 33.2 43.0 44.9 56.6 77.8 82.7 112.6 128.9 134.1 Single-Family 66.2 31.6 24.0 25.0 22.2 27.3 36.3 37.3 44.6 57.5 60.3 Multi-family 40.3 29.2 9.2 18.0 22.6 29.4 41.5 45.3 68.1 71.3 73.9 Nonresidential Permit ValuationNominal (Mil. $) 22626.3 19190.5 10898.1 11173.2 13062.5 14652.9 21813.1 23219.4 23755.6 25642.2 27868.5 %Ch 7.0 -15.2 -43.2 2.5 16.9 12.2 48.9 6.4 2.3 7.9 8.7Real (Mil. 2009$) 23176.2 18819.4 10894.8 11303.4 12817.9 13890.1 20115.1 20888.0 21023.4 21981.5 23158.4 %Ch 0.9 -18.8 -42.1 3.8 13.4 8.4 44.8 3.8 0.6 4.6 5.4

Population (Thous.)Net Inmigration -24.2 -25.2 -89.0 -51.2 -11.0 39.0 45.0 92.0 95.3 128.6 146.2Net Natural Increase 329.9 328.9 310.0 283.0 272.0 258.0 252.0 243.0 240.9 227.2 229.0Population 36552.5 36856.2 37077.2 37306.5 37555.6 37842.4 38136.3 38458.0 38794.3 39142.7 39510.8

REGIONAL MODELING GROUP

UCLA Anderson Forecast, June 2015 Regional Modeling Group–115

The Los Angeles Department of Water and Power (DWP), established at the beginning of the century is the largest municipally-owned utility in the nation. It exists under and by virtue of the Charter of the City of Los Angeles enacted in 1925.

With a work force in excess of 9,000, the DWP provides water and electricity to some 3.5 million residents and businesses in a 464-square-mile area.

DWP’s operations are financed solely by the sale of water and electric services. Capital funds are raised through the sale of bonds. No tax support is received.

A five-member Board of Water and Power Commissioners establishes policy for the DWP. The Board members are appointed by the Mayor and confirmed by the City Council for five-year terms.

REGIONAL MODELING GROUP

116–Regional Modeling Group UCLA Anderson Forecast, June 2015

The Los Angeles County Metropolitan Transportation Authority (Metro) is unique among the nation’s transportation agencies. It serves as transportation planner and coordinator, designer, builder and operator for one of the country’s largest, most populous counties. More than 9 million people – one-third of California’s residents – live, work, and play within its 1,433-square-mile service area.

Besides operating over 2,000 coaches in the Metro Bus fleet, Metro also designed, built and now operates over 73 miles of Metro Rail service. The Metro Rail system currently consists of 62 stations and several more are in the planning and/or design stage.

In addition to operating its own services Metro funds 16 municipal bus operators and funds a wide array of transportation projects including bikeways and pedestrian facilities, local road and highway improvements, goods movement, and the popular Freeway Patrol and Call Boxes.

Recognizing that no one form of transit can solve urban congestion problems, Metro’s multimodal approach uses a variety of transportation alternatives to meet the needs of the highly diverse population in the region.

Metro’s Mission is to insure the continuous improvement of an efficient and effective transportation system for Los Angeles County. In support of this mission, our team members provide expertise and leadership based on their distinct roles: operating transit system elements for which the agency has delivery responsibility, planning the countywide transportation system in cooperation with other agencies, managing the construction and engineering of transportation system components and delivering timely support services to the Metro organization.

Metro was created in the state legislature by Assembly Bill 152 in May 1992. This bill merged the Los Angeles County Transportation Commission (LACTC) and the Southern California Rapid Transit District (RTD) to become the Los Angeles County Metropolitan Transportation Authority. The merger became effective on April 1, 1993.

Metro is governed by a 13-member Board of Directors comprised of: the five Los Angeles County Supervisors, the Mayor of Los Angeles, three Los Angeles mayor-appointed members, four city council members representing the other 87 cities in Los Angeles County and one non-voting member is appointed by the Governor of California.

SEMINAR MEMBERS

UCLA Anderson Forecast, June 2015 Seminar Members–117

The Legislature and Governor created the California Research Bureau (CRB) within the California State Library in the 1991 Budget Act. The bureau provides objective, nonpartisan, timely, and confidential research to the Governor’s Office, members of both houses of the Legislature, and other state constitutional officers. The Bureau provides these clients with research, policy assistance through written reports and other documents, consultations, seminars, and other training and assistance in preparing legislative proposals. The Bureau has five branches: Environmental and Natural Resources; Education and Human Services; Economics; General Law and Government; and Information Services. It maintains a small office at the State Capitol in Room 5210 to make reference services conveniently available.

The nonpartisan Legislative Analyst's Office (LAO) has been providing fiscal and policy advice to the California Legislature for more than 65 years. It is particularly well known for its fiscal and programmatic expertise and nonpartisan analyses relating to the state budget, including making recommendations for operating programs in the most effective and cost-efficient manner possible. Its responsibilities also include making economic and demographic forecasts for California, and fiscal forecasts for state government revenues and expenditures. It also prepares fiscal analyses for all propositions that appear on the California statewide ballot, including bond measures.

For more information about the LAO, please visit our website at www.lao.ca.gov or call us at 916-445-4656.

SEMINAR MEMBERS

118–Seminar Members UCLA Anderson Forecast, June 2015

The Los Angeles Magazine has named Hermosa an "outstanding coastal town" praising many of our businesses and shops. From traditional Surf and Turf to more exotic cuisines, from Comedy to Jazz, Hermosa Beach has many fine dining and entertainment places from which to choose. Our hotel and lodging facilities offer breath taking ocean views and all the comforts of home which are surrounded by a Mecca of restaurants, upscale shops and tourist delights. Come to Hermosa Beach, relax and enjoy the warmth of our hospitality.

City of Hermosa Beach

The State of California’s Department of Finance is responsible for submitting to the State’s fiscal year budget to the Governor in January of each year. The Department is part of the State’s Executive Branch and part of the Governor’s Administration. The Director of Finance is appointed by the Governor and is his chief fiscal advisor. The Director sits as a member of the Governor’s cabinet and senior staff. Principal functions include:

Establish appropriate fiscal policies to carry out the Administration’s Programs.

Prepare, enact and administer the State’s Annual Financial Plan.

Analyze legislation which has a fiscal impact.

Develop and maintain the California State Accounting and Reporting System (CALSTARS).

Monitor/audit expenditures by State departments to ensure compliance with approved standards and policies.

Develop economic forecasts and revenue estimates.

Develop population and enrollment estimates and projections.

Review expenditures on data processing activities of departments.

In addition, the Department of Finance interacts with the Legislature through various reporting requirements, by presenting and defending the Governor’s Budget and in the legislature.

The Department interacts with other State departments on a daily basis on terms of administering the budget, reviewing fiscal proposals, establishing accounting systems, auditing department expenditures and communicating the Governor’s fiscal policy to departments.

SEMINAR MEMBERS

UCLA Anderson Forecast, June 2015 Seminar Members–119

Health Net, Inc. is among the nation’s largest publicly traded managed health care companies. Its mission is to help people be healthy, secure and comfortable. The company’s health plans and government contracts subsidiaries provide health benefits to approximately 6.7 million individuals across the country through group, individual, Medicare, Medicaid and TRICARE and Veterans Affairs programs. Health Net’s behavioral health subsidiary, MHN, provides mental health benefits to approximately 7.0 million individuals in all 50 states. The company’s subsidiaries also offer managed health care products related to prescription drugs, and offer managed health care product coordination for multi-region employers and administrative services for medical groups and self-funded benefits programs.

The Employment Development Department’s Labor Market Information Division (LMID) regularly collects, analyzes, and publishes information about California’s labor market, which has approximately 1,068,000 employers covered by Unemployment Insurance and a civilian labor force of approximately 16.6 million. In addition to employment and unemployment data, LMID provides economic development and planning information; industry and occupational characteristics, trends, and wage information; and social and demographic information. Most of these data are available for the state and counties. Some data are available for other geographic regions a well.

In addition to basic labor market information, the LMID provides technical assistance, training seminars for data users, and standard and customized reports for state and sub-state geographic areas. Labor market information is available electronically and in print.

For more information, visit our website at www.calmis.ca.gov or call 916-262-2162.

SEMINAR MEMBERS

120–Seminar Members UCLA Anderson Forecast, June 2015

The energy industry is changing rapidly and dramatically. As global competition transforms the way companies do business, energy issues are no longer simply local, or even national. At the same time, its clear that the importance of providing reliable local service has never been more important.

Our heritage at Southern California Edison is based on reliability. For more than 100 years we have provided high-quality, reliable electric service to more than 4.2 million business and residential customers over a 50,000 square mile service area in coastal, central, and southern California.

Of course, recent changes in the California’s electric industry have affected us as well. In 1997, as part of the restructuring of the electric industry in our state, SCE sold its 12 fossil fuel generating stations and overhauled nearly every aspect of its business to prepare for the changing environment. While we still own and operate hydro and nuclear power facilities that serve our area, our main role is that of power transmission and distribution. The power needed for our customers is largely purchased from the California Power Exchange and provided by SCE to our customers without a price markup.

At SCE we want you to know that even in times of change, we retain our proven commitment to service, reliability, innovation, and the community.

Celebrating its 150th anniversary in 2014, the Irvine Company is one of America’s most respected and diversified real estate companies. The Company is renowned for its investment properties across coastal California and its stewardship and master planning of The Irvine Ranch in Orange County, California.

The Irvine Company’s property portfolio exceeds 105 million square feet and includes 500 office buildings, 41 retail centers, 130 apartment communities, five marinas, three hotels, and three golf courses, primarily in Orange County, with one-third of the Company’s investment properties in Los Angeles, San Diego, Silicon Valley and Chicago.

As master planner of the historic Irvine Ranch, the Irvine Company plans and brings to life balanced, sustainable communities with a full range of housing, job and retail centers, schools, recreation and permanently preserved open spaces. Nearly 60% of the 93,000-acre Irvine Ranch — or 55,000 acres — has been preserved in perpetuity as parklands and open space.

Donald Bren is Chairman of the Irvine Company. He has been deeply involved in California real estate as a master planner, master builder and long-term investor for more than 50 years. He oversees a Board of Directors that includes some of the nation’s most accomplished and respected business leaders and former public officials.

UCLA Anderson Forecast, June 2015 Sponsors-121

122-Sponsors UCLA Anderson Forecast, June 2015

MEMBERS

UCLA Anderson Forecast, June 2015 Members - 123

Corporate 6

California Energy CommissionThe California Endowment

Corporate 4

ADPCFA Society Los AngelesCity National Bank - CosciaCity of Los AngelesIS AssociatesIBIS World, Inc.Southern California Assoc of Governments

Corporate 3

Ameron InternationalCitizens Business BankCity of El SegundoCity of Manhattan BeachCity of Santa MonicaHanmi BankKia Motors America, Inc.Korea Trade Invt Promotion AgencyLos Angeles Police Federal Credit UnionMcMaster-Carr Supply CompanyMetropolitan Water DistrictMitsubishi Cement Corp.Pacific Western BankPepperdine UniversityRPAState Farm Insurance Co.The Newhall Land and Farming CompanyUnified Grocers, Inc.WCIRBWinreal Operating Co.

Individual Member

ALGAlliance BernsteinAustrian Trade CommissionBBCN BankBrand Management IncBRE Properties, INCCal RecycleCalifornia Air Resources BoardCalifornia Association Of RealtorsCalifornia Department of TransportationCalifornia Public Utilities CommissionCalifornia State Board of EqualizationCalifornia State Polytechnic University, PomonaCalifornia State University, SacramentoCalifornia Steel Industries, IncCathay BankChartwell Capital SolutionsChicago TitleChu & Waters, LLPCity of CarlsbadCity of Garden GroveCity Of SacramentoCity of San DiegoCity of San JoseCity of Santa ClaraCity of TorranceCity of Torrance - Kenneth FlewellynCommunity BankConsulate General of JapanCounty of San DiegoCTBC Bank USADesmond, Marcello & AmsterEast West BankFDICGoodwin Procter LLP

MEMBERS

124 - Members UCLA Anderson Forecast, June 2015

Granite Rock CompanyHarold Davidson & Associates Inc.Heritage Bank of CommerceHoward HsiehHR and A Advisors, Inc.JETRO, Los AngelesKinecta Federal Credit UnionKPMGLehigh Southwest Cement CompanyLloyd Management CorporationLogix Federal Credit UnionLondre Marketing Consultants, LLCLos Angeles Public Library - Business Economics DeptMaynard Consulting ServicesNewland Real Estate GroupNorthern California Power AgencyOrange County Executive Office - BudgetOrange County Transportation Authority 2Pasadena Public LibraryPG&EPreferred Employers Insurance Company

RBC Capital MarketsRedwood Credit UnionSan Diego Gas & Electric Co.School Services of California Inc.SMUDStanford UniversityState Compensation Insurance FundState of Hawaii - Department of TaxationSully-Miller Contracting CoThe Aerospace CorporationThe Olson CompanyUnited Methodist F.C.U.University of California Library, BerkeleyUniversity of California San DiegoUniversity of CincinnatiUniversity of RichmondUSS-POSCO IndustriesVulcan Materials CompanyWarland InvestmentsWells Fargo SecuritiesYork University Libraries

SPEAKERS

UCLA Anderson Forecast, June 2015 Speakers–125

Edward E. Leamer is the Chauncey J. Medberry Professor of Management, Professor of Economics and Professor of Statistics at UCLA. He received a B.A. degree in mathematics from Princeton University and a Ph.D. degree in economics and an M.A. degree in mathematics from the University of Michigan. After serving as Assistant and Associate Professor at Harvard University he joined the University of California at Los Angeles in 1975 as Professor of Economics and served as Chair from 1983 to 1987.

In 1990 he moved to the Anderson Graduate School of Management and was appointed to the Chauncey J. Medberry Chair. Professor Leamer is a Fellow of the American Academy of Arts and Sciences, and a Fellow of the Econometric Society. He is a Research Associate of the National Bureau of Economic Research and a visiting scholar at the International Monetary Fund and the Board of Governors of the Federal Reserve System. Dr. Leamer has published over 100 articles and 4 books . This research has been supported by continuous grants for over 25 years from the National Science Foundation, the Sloan Foundation and the Russell Sage Foundation. His research papers in econometrics have been collected in Sturdy Econometrics, published in the Edward Elgar Series of Economists of the 20th Century. His research in international economics and econometric methodology has been discussed in a chapter written by Herman Leonard and Keith Maskus in New Horizons in Economic Thought: Appraisals of Leading Economists. Recent research interests of Professor Leamer include the North American Free Trade Agreement, the dismantling of the Swedish welfare state, the economic integration of Eastern Europe, Taiwan and the Mainland, and the impact of globalization on the U.S. economy.

Edward E. LeamerDirector

David ShulmanSenior Economist

David Shulman is currently managing member of his own LLC and engages in educational and charitable ac-tivities, including being a Distinguished Visiting Professor at Baruch College and a Visiting Professor at the Univer-sity of Wisconsin. Dr. Shulman is currently a member of NAREIT’s Real Estate Investment Advisory Council. He blogs at Shulmaven.blogspot.com. Shulman received a bachelor’s degree from Baruch College in 1965, an MBA in 1966 from the Graduate School of Management at UCLA; and his Ph.D. in 1975 with a specialization in Finance.

From 1986 to 1997, Dr. Shulman was employed by Sa-lomon Brothers Inc. in various capacities. He was their director of real estate research from 1987 to 1991 and be-came Chief Equity Strategist from 1992 to 1997. As Chief Equity Strategist, he was responsible for developing the firms overall equity market view and maintaining their list of recommended stocks. Dr. Shulman was widely quoted in print and electronic media and he coined the terms “Gold-ilocks Economy” and “New Paradigm Economy.” In 1991, he was named a Managing Director; and in 1990, he won the First Annual Graaskamp Award for Excellence in real estate research from the Pension Real Estate Association.

In March 2005, Dr. Shulman retired from Lehman Broth-ers, where he was Managing Director and head Real Estate Investment Trust Analyst. Before joining Lehman Brothers in 2000, he was a member and Senior Vice President at Ulysses Management LLC from 1998-1999, an Investment Manager of a private investment partnership and an offshore corporation, whose invest-ment capital approximated $1 billion at the end of 1999.

SPEAKERS

126–Speakers UCLA Anderson Forecast, June 2015

Jerry NickelsburgSenior Economist

Jerry Nickelsburg joined the UCLA Anderson Forecast in 2006 as an economist. At the Anderson Forecast he plays a key role in the economic modeling and forecasting of the Los Angeles, Southern California and California economies. He has conducted special studies into the future of manufacturing in Los Angeles, the distribution of income, the economic impact of the writer’s strike, the aerospace industry, the undocumented construction and manufacturing labor force, the ports of Los Angeles and Long Beach and the garment industry, focusing on the development of new data and the application of economic theory and statistical methods to sectoral issues. He is a regular presenter at the Los Angeles Mayor’s Economic Conference and has been cited in the national and local media including the Financial Times, New York Times, Los Angeles Times, Reuters, Variety, CNBC, NBC, PBS, and L.A. Business Journal.

He received his Ph.D. in economics from the University of Minnesota in 1980 specializing in monetary economics and econometrics. He was formerly a professor of Economics at the University of Southern California and has held executive positions with McDonnell Douglas, Flight Safety International, and Flight Safety Boeing during a fifteen year span in the aviation business.

From 2000 to 2006, he was the Managing Principal of Deep Blue Economics, a consulting firm he founded. He held a position with the Federal Reserve Board of Governors developing forecasting tools, and has advised banks, investors and financial institutions. He has been the recipient of the Korda Fellowship, USC Outstanding Teacher, India Chamber of Commerce Jubilee Lecturer and is a Fulbright Scholar. He has published over 40 articles on monetary economics, econometrics, aviation economics, and industrial organization.

William YuEconomist

William Yu joined the UCLA Anderson Forecast in 2011 as an economist. At Forecast he focuses on the economic modeling and forecasting of Los Angeles and other regional economies in California. He also conducts research and forecast on Asian emerging economies, especially China, and their impacts on the US economy. His research interests include a wide range of economic and financial issues, such as time series econometrics, stock, bond and commodity price dynamics, public health, human capital, higher education, and economic sustainability. He has published over a dozen research articles in Journal of Forecasting, International Journal of Forecasting, Journal of International Money and Finance, Journal of Health Care Finance, Journal of Education Finance, Economic Affairs, and Global Economic Review, etc. He has also served as a reviewer for various journals, such as Journal of Money, Credit, and Banking, Journal of Banking and Finance, Japan and the World Economy, and Energy Journal, etc.

He received his bachelor’s degree in finance from National Taiwan University in 1995 and was an analyst in Fubon Financial Holding in Taipei from 1997 to 2000. In 2006, he received his Ph.D. degree in economics from the University of Washington where he was also an economics instructor and won two distinguished teaching awards. In 2006, he worked for the Frank Russell Investment Group for Treasury and corporate yields modeling and forecasting. From 2006 to 2011, he served as an assistant and an associate professor of economics at Winona State University where he taught courses including international economics, forecasting methods, intermediate macroeconomics, introductory macroeconomics, money and banking, and Asian economies.

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THE UCLA ANDERSON FORECAST FOR THE NATION ......Sources: Federal Reserve Board and UCLA Anderson...

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